Enterprise data management dashboard

ABSTRACT

Various embodiments described herein relate to an enterprise data management dashboard. In this regard, a request to generate a dashboard visualization related to one or more assets is received. The request includes an asset descriptor describing the one or more assets. In response to the request, aspects of aggregated operational technology data within a knowledge graph data structure are correlated to provide one or more insights associated with the one or more assets. Additionally, the dashboard visualization is provided to an electronic interface of a computing device. The dashboard visualization includes visualization data for the one or more insights associated with the knowledge graph data structure.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(a) of IndiaPatent Application No. 202111059054, titled “INDUSTRIAL KNOWLEDGE GRAPHAND CONTEXTUALIZATION,” and filed on Dec. 17, 2021, India PatentApplication No. 202111059053, titled “ARTIFICIAL INTELLIGENCE SYSTEM FORINTEGRITY OPERATING WINDOW OPTIMIZATION,” and filed on Dec. 17, 2021,India Patent Application No. 202111059052, titled “ENTERPRISE DATAMANAGEMENT DASHBOARD,” and filed on Dec. 17, 2021, and India PatentApplication No. 202111059109, titled “ENTERPRISE DATA MANAGEMENT,” andfiled on Dec. 17, 2021, which applications are hereby incorporated byreference in their entireties.

TECHNICAL FIELD

The present disclosure relates generally to real-time asset analytics,and more particularly to real-time asset analytics for industrialassets.

BACKGROUND

Traditionally, data analytics and/or digital transformation of datarelated to assets generally involve human interaction. However, oftentimes a specialized worker (e.g., a manager) is responsible for a largenumber of assets (e.g., a large number of industrial assets in anindustrial plant). Therefore, it is generally difficult to identifyand/or fix issues with the assets. For example, in certain scenarios,multiple assets (e.g., 25 assets) within an industrial plant may have anissue. Assets in enterprise environments (e.g., industrial plants, etc.)are also often implemented in connection with multiple systems and/orapplications. However, determining inter-relationships between data fromthe multiple systems and/or applications is generally difficult,inefficient, and/or time consuming. Furthermore, a limited amount oftime is traditionally spent on modeling of data related to assets to,for example, provide insights related to the data. As such, computingresources related to data analytics and/or digital transformation ofdata related to assets are traditionally employed in an inefficientmanner.

SUMMARY

The details of some embodiments of the subject matter described in thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

In an embodiment, a system comprises one or more processors, a memory,and one or more programs stored in the memory. In one or moreembodiments, the one or more programs comprise instructions configuredto receive a request to generate a dashboard visualization related toone or more assets. In one or more embodiments, the request comprises anasset descriptor describing the one or more assets. In one or moreembodiments, in response to the request, the one or more programscomprise instructions configured to correlate, based on the assetdescriptor, aspects of aggregated operational technology data within aknowledge graph data structure to provide one or more insightsassociated with the one or more assets. In one or more embodiments, inresponse to the request, the one or more programs also compriseinstructions configured to provide the dashboard visualization to anelectronic interface of a computing device, the dashboard visualizationcomprising visualization data for the one or more insights associatedwith the knowledge graph data structure. In one or more embodiments, inresponse to the request, the one or more programs comprise instructionsconfigured to adjust one or more operational settings for the one ormore assets based on the dashboard visualization.

In another embodiment, a method comprises, at a device with one or moreprocessors and a memory, receiving a request to generate a dashboardvisualization related to one or more assets. In one or more embodiments,the request comprises an asset descriptor describing the one or moreassets. In one or more embodiments, in response to the request, themethod comprises correlating, based on the asset descriptor, aspects ofaggregated operational technology data within a knowledge graph datastructure to provide one or more insights associated with the one ormore assets. In one or more embodiments, in response to the request, themethod also comprises providing the dashboard visualization to anelectronic interface of a computing device, the dashboard visualizationcomprising visualization data for the one or more insights associatedwith the knowledge graph data structure. In one or more embodiments, themethod also comprises adjusting one or more operational settings for theone or more assets based on the dashboard visualization.

In yet another embodiment, a non-transitory computer-readable storagemedium comprises one or more programs for execution by one or moreprocessors of a device. The one or more programs comprise instructionswhich, when executed by the one or more processors, cause the device toreceive a request to generate a dashboard visualization related to oneor more assets. In one or more embodiments, the request comprises anasset descriptor describing the one or more assets. In one or moreembodiments, the one or more programs comprise instructions which, whenexecuted by the one or more processors and in response to the request,cause the device to correlate, based on the asset descriptor, aspects ofaggregated operational technology data within a knowledge graph datastructure to provide one or more insights associated with the one ormore assets. In one or more embodiments, the one or more programs alsocomprise instructions which, when executed by the one or more processorsand in response to the request, cause the device to provide thedashboard visualization to an electronic interface of a computingdevice, the dashboard visualization comprising visualization data forthe one or more insights associated with the knowledge graph datastructure. In one or more embodiments, the one or more programs alsocomprise instructions which, when executed by the one or moreprocessors, cause the device to adjust one or more operational settingsfor the one or more assets based on the dashboard visualization.

BRIEF DESCRIPTION OF THE DRAWINGS

The description of the illustrative embodiments can be read inconjunction with the accompanying figures. It will be appreciated thatfor simplicity and clarity of illustration, elements illustrated in thefigures have not necessarily been drawn to scale. For example, thedimensions of some of the elements are exaggerated relative to otherelements. Embodiments incorporating teachings of the present disclosureare shown and described with respect to the figures presented herein, inwhich:

FIG. 1 illustrates an exemplary networked computing system environment,in accordance with one or more embodiments described herein;

FIG. 2 illustrates a schematic block diagram of a framework of an IoTplatform of the networked computing system, in accordance with one ormore embodiments described herein;

FIG. 3 illustrates a system that provides an exemplary environment, inaccordance with one or more embodiments described herein;

FIG. 4 illustrates another system that provides an exemplaryenvironment, in accordance with one or more embodiments describedherein;

FIG. 5 illustrates an exemplary computing device, in accordance with oneor more embodiments described herein;

FIG. 6 illustrates a system for generating a knowledge graph, inaccordance with one or more embodiments described herein;

FIG. 7 illustrates a system related to a cognitive advisor, inaccordance with one or more embodiments described herein;

FIG. 8 illustrates a system related to a dashboard visualization, inaccordance with one or more embodiments described herein;

FIG. 9 illustrates an exemplary knowledge graph, in accordance with oneor more embodiments described herein;

FIG. 10 illustrates a system for providing insights related toenterprise data management, in accordance with one or more embodimentsdescribed herein;

FIG. 11 illustrates an exemplary process visualization, in accordancewith one or more embodiments described herein;

FIG. 12 illustrates a flow diagram for generating a knowledge graph forone or more assets, in accordance with one or more embodiments describedherein;

FIG. 13 illustrates a flow diagram for integrity operating windowoptimization for one or more assets, in accordance with one or moreembodiments described herein;

FIG. 14 illustrates a flow diagram for integrity operating windowoptimization for one or more assets, in accordance with one or moreembodiments described herein; and

FIG. 15 illustrates a functional block diagram of a computer that may beconfigured to execute techniques described in accordance with one ormore embodiments described herein.

DETAILED DESCRIPTION

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments. The term “or” is usedherein in both the alternative and conjunctive sense, unless otherwiseindicated. The terms “illustrative,” “example,” and “exemplary” are usedto be examples with no indication of quality level. Like numbers referto like elements throughout.

The phrases “in an embodiment,” “in one embodiment,” “according to oneembodiment,” and the like generally mean that the particular feature,structure, or characteristic following the phrase can be included in atleast one embodiment of the present disclosure, and can be included inmore than one embodiment of the present disclosure (importantly, suchphrases do not necessarily refer to the same embodiment).

The word “exemplary” is used herein to mean “serving as an example,instance, or illustration.” Any implementation described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other implementations.

If the specification states a component or feature “can,” “may,”“could,” “should,” “would,” “preferably,” “possibly,” “typically,”“optionally,” “for example,” “often,” or “might” (or other suchlanguage) be included or have a characteristic, that particularcomponent or feature is not required to be included or to have thecharacteristic. Such component or feature can be optionally included insome embodiments, or it can be excluded.

In general, the present disclosure provides for an “Internet-of-Things”or “IoT” platform for enterprise performance management that usesreal-time accurate models and visual analytics to deliver intelligentactionable recommendations for sustained peak performance of anenterprise or organization. The IoT platform is an extensible platformthat is portable for deployment in any cloud or data center environmentfor providing an enterprise-wide, top to bottom view, displaying thestatus of processes, assets, people, and safety. Further, the IoTplatform of the present disclosure supports end-to-end capability toexecute digital twins against process data and to translate the outputinto actionable insights, as detailed in the following description.

Traditionally, data analytics and/or digital transformation of datarelated to assets generally involves human interaction. However, oftentimes a specialized worker (e.g., a manager) is responsible for a largenumber of assets (e.g., a large number of industrial assets in anindustrial plant). Therefore, it is generally difficult to identifyand/or fix issues with the assets. For example, in certain scenarios,multiple assets (e.g., 25 assets) within an industrial plant may have anissue. Assets in enterprise environments (e.g., industrial plants, etc.)are also often implemented in connection with multiple systems and/orapplications. However, determining inter-relationships between data fromthe multiple systems and/or applications is generally difficult,inefficient, and/or time consuming. Furthermore, a limited amount oftime is traditionally spent on modeling of data related to assets to,for example, provide insights related to the data. As such, computingresources related to data analytics and/or digital transformation ofdata related to assets are traditionally employed in an inefficientmanner.

Thus, to address these and/or other issues, technologies and/ortechniques disclosed herein are employed to provide enterprise datamanagement related to one or more assets. In various embodiments, aknowledge graph related to one or more assets is provided. Additionallyor alternatively, in various embodiments, the technologies and/ortechniques disclosed herein are employed to provide contextualization ofdata for a knowledge graph related to one or more assets. In one or moreembodiments, the knowledge graph is an industrial knowledge graphrelated to one or more industrial assets. In one or more embodiments, anartificial intelligence system for integrity operating windowoptimization for one or more assets is provided. In various embodiments,the artificial intelligence system is a cognitive advisor for enterprisedata management. In one or more embodiments, the artificial intelligencesystem employs a knowledge graph related to the one or more assets. Inone or more embodiments, the knowledge graph is an industrial knowledgegraph related to one or more industrial assets. In one or moreembodiments, an enterprise data management dashboard is provided.

In one or more embodiments, the knowledge graph is constructed based onindividual relationships configured for the one or more assets and/or inone or more systems related to the one or more assets. As such, invarious embodiments, the knowledge graph derives one or morerelationships that are not directly available in source systemsconfiguration datasets for the one or more assets. In one or moreembodiments, the knowledge graph is constructed using a configuration ofrules for one or more data sources associated with the one or moreassets. In one or more embodiments, the knowledge graph captures unifieddata and relationships for an industrial plant. Accordingly, in one ormore embodiments, a pre-indexed industrial search is performed withrespect to the knowledge graph to provide one or more insights withrespect to the one or more assets. In one or more embodiments, theknowledge graph is constructed based on input specific to a sourcesystem type and/or a file format for data associated with the one ormore assets. For example, in one or more embodiments, configurationinformation for one or more systems of an industrial plant that includesthe one or more assets is employed to construct the knowledge graphand/or to contextualize data for the knowledge graph. In one or moreembodiments, the knowledge graph provides a consolidated mapping ofoperational technology data associated with monitoring and/or control ofthe one or more assets.

In one or more embodiments, the knowledge graph is employed to adjustone or more integrity operating windows (e.g., one or more operationalboundaries) for one or more processes related to the one or more assets.For example, in one or more embodiments, one or more operating conditioninsights with respect to the knowledge graph are employed to applydynamic limits (e.g., adaptive thresholding) for one or more processesrelated to the one or more assets. In one or more embodiments, acognitive advisor dynamically optimizes one or more integrity operatingwindows) for one or more processes related to the one or more assets.Additionally, in one or more embodiments, the cognitive advisor providesone or more notifications associated with an early warning before actualoperations for the one or more processes deviate from a set operatinglimit. In one or more embodiments, the cognitive advisor provides aprediction for future industrial plant state and/or a prediction for aroot causes of the future industrial plant state. Additionally, in oneor more embodiments, the cognitive advisor provides one or morerecommendations regarding how to fix a corresponding issue for an assetand/or process with a certain degree of confidence. As such, in variousembodiments, the cognitive advisor provides a prediction of a certainindustrial plant state by employing the knowledge graph to identify oneor more events occurring with respect to the one or more processes. Invarious embodiments, the knowledge graph models relationships betweenend points (e.g., to provide an influence/dependency flow view of theone or more processes related to the one or more assets). In variousembodiments, the cognitive advisor determines whether impliedrelationships associated with the knowledge graph are correlated (e.g.,using historical data) and/or adds ontological relationship data to theknowledge graph in response to determining that the correlation is abovea threshold value. As such, in various embodiments, the cognitiveadvisor identifies end points in the knowledge graph with a certaindegree of significance for an asset to, for example, improve performanceof the asset and/or a process performed by the asset.

In one or more embodiments, a visualization interface (e.g., dashboardvisualization) presents information associated with the knowledge graphand/or the one or more insights associated with the one or more assets.In one or more embodiments, the visualization interface is configured toallow an end user at an industrial plant (e.g., an application engineer,a remote support engineer, another end user, etc.) to navigate thevisualization interface, access information associated with theknowledge graph, access information associated with the one or moreinsights, and/or troubleshoot issues associated with the one or moreassets without expertise knowledge of existing systems and/or tagsassociated with the one or more assets. In one or more embodiments, ascheduled analytics engine employs the knowledge graph to process one ormore limits, one or more deviations, one or more targets, and/or otherdata associated with the one or more assets and/or one or more processesrelated to the one or more assets. In one or more embodiments, one ormore recommendations are automatically generated based on the one ormore insights associated with the knowledge graph to correct one or morelimits, one or more thresholds, one or more tolerances, and/or othersettings for the one or more assets.

In various embodiments, operational technology data associated with oneor more assets is ingested, cleaned and aggregated to provide aggregatedoperational technology data. Furthermore, in various embodiments, one ormore metrics are determined from the aggregated operational technologydata to provide opportunity and/or performance insights for the assets.According to various embodiments, a dashboard visualization thatpresents issues associated with the one or more assets is provided. Invarious embodiments, the dashboard visualization is an enterpriseapplication that allows a portfolio operator to remotely manage,investigate, and/or resolve issues associated with the one or moreassets. In various embodiments, features, attributes and/orrelationships associated with the aggregated operational technology dataare determined based on the knowledge graph to, for example,trouble-shoot equipment faults, control equipment, and/or changeset-points to resolve issues within the dashboard visualization. Invarious embodiments, the dashboard visualization facilitates aggregationof operational technology data into a score or metric value such as, forexample, a key performance indicator (KPI). In various embodiments, thedashboard visualization additionally or alternatively facilitatesproviding recommendations to improve asset performance. In variousembodiments, the dashboard visualization additionally or alternativelyfacilitates remote control and/or altering of asset set points. In oneor more embodiments, the issues associated with the one or more assetsare ordered such that issues with a largest impact with respect to theportfolio of assets is presented first via the dashboard visualization.Impact may be based on cost to repair an asset, energy consumptionassociated with issues related to the one or more assets, savings lostassociated with issues related to the one or more assets, etc.

In various embodiments, a user may employ the dashboard visualization toidentify issues associated with the one or more assets, to makeadjustments with respect to the one or more assets, and/or to make workorders associated with the one or more assets. In various embodiments, auser may be subscribed to a performance management category (e.g.,Energy Optimization, Digitized Maintenance, etc.) to facilitatedetermining issues for the one or more assets to be resolved and/or tofacilitate determining an ordering for prioritized actions related tothe one or more assets. For example, an ordering of prioritized actionsmay be different for Energy Optimization than Digitized Maintenance. Invarious embodiments, the dashboard visualization provides an alerts listthat combines alerts from an on-premise building management system(BMS). In various embodiments, cloud analytics is performed to groupalerts based on issues and/or to prioritize the issues based on one ormore algorithms. As such, according to various embodiments, asset and/orworkforce use is optimized, and highest priority issues related to theone or more assets is presented to a user in an optimal manner.Additionally, according to various embodiments, facility operatingand/or maintenance costs are reduced while also improving equipmentup-time, service operational efficiency, and/or environmental conditionsby employing the dashboard visualization. Additionally, by employing thedashboard visualization according to various embodiments, remote triageof faults and/or remote resolution of asset issues is provided. Invarious embodiments, the dashboard visualization provides real-timeasset analytics associated with sensors (e.g., vibration, power, etc.),control devices (e.g., key performance indicators (KPIs), equipmentstates, etc.), labor management (e.g., allocation, utilization, quality,etc.), warehouse execution (e.g., orders, routing, etc.), inventorymanagement (e.g., location, quantity, slotting, etc.), and/or one ormore other layers of an enterprise. Additionally, according to variousembodiments, the dashboard visualization provides centralized capabilityto review, manage and/or control assets.

In various embodiments, the knowledge graph is employed to provideautomated display of real-time properties and trends related to servicecases into tabular and graphical displays. Additionally oralternatively, in various embodiments, an knowledge graph is employed toprovide automated generation and display of equipment schematic diagramsand configurations using standard or modular diagrams populated by modeldata. Additionally or alternatively, in various embodiments, theknowledge graph is employed to create a graph model view ofrelationships between the one or more assets (e.g., between equipmentand/or other assets in the facilities, between building and physicalspaces within buildings, etc.). Additionally or alternatively, invarious embodiments, the knowledge graph is employed to determinerelationships between models such that nodes in the graph visuallyindicates whether the portfolio of assets is associated with one or morealarms related to the nodes. Additionally or alternatively, in variousembodiments, the knowledge graph is employed to provide informationnotifications via the nodes with asset data and/or links to otherinformation.

In various embodiments, the dashboard visualization facilitates displayof graphics and/or other visualizations related to the one or moreassets. For example, in various embodiments, the dashboard visualizationprovides dynamically generated graphics that show configuration of,relationships between, and/or location of assets to, for example, enableknowledge associated with remote facilities, aiding of fault diagnosis,and/or performing actions related to issues. In various embodiments, thedashboard visualization facilitates operations and/or schedulingassociated with the one or more assets. For example, in variousembodiments, the dashboard visualization facilitate temporary orlong-term changes to operational modes of assets can be made throughscheduling changes and/or manual switching to allow for events, seasonalchanges, maintenance periods and/or other changes to asset use oroperations.

In various embodiments, the dashboard visualization presents alerts fromdifferent sources and/or different system types into a single alertscreen to provide a prioritized view of issues related to the one ormore assets. According to various embodiments, the alerts include alarmsfrom on-premises BMS, security, fire and other systems. Additionally oralternatively, according to various embodiments, the alerts includealerts from analytics and/or rule-based cloud-located systems withrespect to current states and/or historical states of assets.Additionally or alternatively, according to various embodiments, thealerts include alerts from systems monitoring an asset environmentand/or health and safety conditions associated with the one or moreassets. Additionally or alternatively, according to various embodiments,the alerts include alerts from cyber security systems. Additionally oralternatively, according to various embodiments, the alerts includealerts from systems monitoring of the health of the one or more assets.In various embodiments, the alerts are logically grouped and/orpresented to an operator via the dashboard visualization. In variousembodiments, the alerts are logically grouped based on location (e.g.,geographic areas or buildings) and/or related assets. In variousembodiments, the alerts are presented via the dashboard visualizationsuch that the highest priority issues are at the top of the list ofalerts. In various embodiments, prioritization of the alerts isdetermined based on type of asset, type of facility, use and size ofarea affected by the issues, number of assets, number of issues, typesassigned priority of individual alerts, and/or other features associatedwith the assets. In various embodiments, machine learning is employed tologically grouped and/or present the alerts. In various embodiments,machine learning is employed to identify alerts that optimally reflectuse by an operator of the dashboard visualization.

In various embodiments, a dashboard visualization across various useridentities is provided via a templated dashboard model using theknowledge graph. In various embodiments, a dashboard visualization for aparticular user identity (e.g., a maintenance is reported at varioushierarchy levels such as an enterprise level, a site level, a plantlevel, a unit level (e.g., an asset level), etc. In various embodiments,metrics associated with a first asset hierarchy level (e.g., anenterprise level) includes metrics or goals (e.g., OEE, etc.). Invarious embodiments, metrics associated with a second asset hierarchylevel (e.g., a site level) includes metrics that influence a target goal(e.g., availability, energy, performance, quality). In variousembodiments, metrics associated with a third asset hierarchy level(e.g., a plant level) includes identification of undesirable actorassets that influences targeted goal OEE. In various embodiments,metrics associated with a fourth asset hierarchy level (e.g., an assetlevel) includes events or exception that are related to a target goal.

In various embodiments, a dashboard visualization is modified based oncontext (e.g., a dashboard is changed to energy context and displays asame level of details based on modelling of assets and/or metrics viathe knowledge graph). In various embodiments, the knowledge graph isemployed to present relevant metrics based on user role, user context ofinvoking the dashboard, and/or hierarchy mapped for a metrics model. Invarious embodiments, an application programming interface is employed tointegrate different visualization tools and/or different reporting tools(e.g., via the dashboard visualization). In one or more embodiments, auser-interactive graphical user interface is generated. For instance, inone or more embodiments, the graphical user interface renders a visualrepresentation of the dashboard visualization. In one or moreembodiments, one or more notifications for user devices are generatedbased on metrics associated with the knowledge graph.

As such, by employing one or more techniques disclosed herein,enterprise data management and/or asset performance is optimized.Moreover, by employing a knowledge graph and/or one or more techniquesdisclosed herein, improved insights for opportunity and/or performanceinsights for assets is provided to a user. For instance, by employing aknowledge graph and/or one or more techniques disclosed herein,additional and/or improved asset insights as compared to capabilities ofconventional techniques can be achieved across a data set. Additionally,performance of a processing system associated with data analytics isimproved by employing one or more techniques disclosed herein. Forexample, a number of computing resources, a number of a storagerequirements, and/or number of errors associated with data analytics isreduced by employing one or more techniques disclosed herein. Inaddition, by employing a knowledge graph and/or one or more techniquesdisclosed herein, cost savings for one or assets and/or one or moreasset enterprises is provided.

FIG. 1 illustrates an exemplary networked computing system environment100, according to the present disclosure. As shown in FIG. 1 , networkedcomputing system environment 100 is organized into a plurality of layersincluding a cloud 105 (e.g., cloud layer 105), a network 110 (e.g.,network layer 110), and an edge 115 (e.g., edge layer 115). As detailedfurther below, components of the edge 115 are in communication withcomponents of the cloud 105 via network 110.

In various embodiments, network 110 is any suitable network orcombination of networks and supports any appropriate protocol suitablefor communication of data to and from components of the cloud 105 andbetween various other components in the networked computing systemenvironment 100 (e.g., components of the edge 115). According to variousembodiments, network 110 includes a public network (e.g., the Internet),a private network (e.g., a network within an organization), or acombination of public and/or private networks. According to variousembodiments, network 110 is configured to provide communication betweenvarious components depicted in FIG. 1 . According to variousembodiments, network 110 comprises one or more networks that connectdevices and/or components in the network layout to allow communicationbetween the devices and/or components. For example, in one or moreembodiments, the network 110 is implemented as the Internet, a wirelessnetwork, a wired network (e.g., Ethernet), a local area network (LAN), aWide Area Network (WANs), Bluetooth, Near Field Communication (NFC), orany other type of network that provides communications between one ormore components of the network layout. In some embodiments, network 110is implemented using cellular networks, satellite, licensed radio, or acombination of cellular, satellite, licensed radio, and/or unlicensedradio networks.

Components of the cloud 105 include one or more computer systems 120that form a so-called “Internet-of-Things” or “IoT” platform 125. Itshould be appreciated that “IoT platform” is an optional term describinga platform connecting any type of Internet-connected device, and shouldnot be construed as limiting on the types of computing systems useablewithin IoT platform 125. In particular, in various embodiments, computersystems 120 includes any type or quantity of one or more processors andone or more data storage devices comprising memory for storing andexecuting applications or software modules of networked computing systemenvironment 100. In one embodiment, the processors and data storagedevices are embodied in server-class hardware, such as enterprise-levelservers. For example, in an embodiment, the processors and data storagedevices comprise any type or combination of application servers,communication servers, web servers, super-computing servers, databaseservers, file servers, mail servers, proxy servers, and/virtual servers.Further, the one or more processors are configured to access the memoryand execute processor-readable instructions, which when executed by theprocessors configures the processors to perform a plurality of functionsof the networked computing system environment 100.

Computer systems 120 further include one or more software components ofthe IoT platform 125. For example, in one or more embodiments, thesoftware components of computer systems 120 include one or more softwaremodules to communicate with user devices and/or other computing devicesthrough network 110. For example, in one or more embodiments, thesoftware components include one or more modules 141, models 142, engines143, databases 144, services 145, and/or applications 146, which may bestored in/by the computer systems 120 (e.g., stored on the memory), asdetailed with respect to FIG. 2 below. According to various embodiments,the one or more processors are configured to utilize the one or moremodules 141, models 142, engines 143, databases 144, services 145,and/or applications 146 when performing various methods described inthis disclosure.

Accordingly, in one or more embodiments, computer systems 120 execute acloud computing platform (e.g., IoT platform 125) with scalableresources for computation and/or data storage, and may run one or moreapplications on the cloud computing platform to perform variouscomputer-implemented methods described in this disclosure. In someembodiments, some of the modules 141, models 142, engines 143, databases144, services 145, and/or applications 146 are combined to form fewermodules, models, engines, databases, services, and/or applications. Insome embodiments, some of the modules 141, models 142, engines 143,databases 144, services 145, and/or applications 146 are separated intoseparate, more numerous modules, models, engines, databases, services,and/or applications. In some embodiments, some of the modules 141,models 142, engines 143, databases 144, services 145, and/orapplications 146 are removed while others are added.

The computer systems 120 are configured to receive data from othercomponents (e.g., components of the edge 115) of networked computingsystem environment 100 via network 110. Computer systems 120 are furtherconfigured to utilize the received data to produce a result. Accordingto various embodiments, information indicating the result is transmittedto users via user computing devices over network 110. In someembodiments, the computer systems 120 is a server system that providesone or more services including providing the information indicating thereceived data and/or the result(s) to the users. According to variousembodiments, computer systems 120 are part of an entity which includeany type of company, organization, or institution that implements one ormore IoT services. In some examples, the entity is an IoT platformprovider.

Components of the edge 115 include one or more enterprises 160 a-160 neach including one or more edge devices 161 a-161 n and one or more edgegateways 162 a-162 n. For example, a first enterprise 160 a includesfirst edge devices 161 a and first edge gateways 162 a, a secondenterprise 160 b includes second edge devices 161 b and second edgegateways 162 b, and an nth enterprise 160 n includes nth edge devices161 n and nth edge gateways 162 n. As used herein, enterprises 160 a-160n represent any type of entity, facility, or vehicle, such as, forexample, companies, divisions, buildings, manufacturing plants,warehouses, real estate facilities, laboratories, aircraft, spacecraft,automobiles, ships, boats, military vehicles, oil and gas facilities, orany other type of entity, facility, and/or entity that includes anynumber of local devices.

According to various embodiments, the edge devices 161 a-161 n representany of a variety of different types of devices that may be found withinthe enterprises 160 a-160 n. Edge devices 161 a-161 n are any type ofdevice configured to access network 110, or be accessed by other devicesthrough network 110, such as via an edge gateway 162 a-162 n. Accordingto various embodiments, edge devices 161 a-161 n are “IoT devices” whichinclude any type of network-connected (e.g., Internet-connected) device.For example, in one or more embodiments, the edge devices 161 a-161 ninclude assets, sensors, actuators, processors, computers, valves,pumps, ducts, vehicle components, cameras, displays, doors, windows,security components, boilers, chillers, pumps, HVAC components, factoryequipment, and/or any other devices that are connected to the network110 for collecting, sending, and/or receiving information. Each edgedevice 161 a-161 n includes, or is otherwise in communication with, oneor more controllers for selectively controlling a respective edge device161 a-161 n and/or for sending/receiving information between the edgedevices 161 a-161 n and the cloud 105 via network 110. With reference toFIG. 2 , in one or more embodiments, the edge 115 include operationaltechnology (OT) systems 163 a-163 n and information technology (IT)applications 164 a-164 n of each enterprise 161 a-161 n. The OT systems163 a-163 n include hardware and software for detecting and/or causing achange, through the direct monitoring and/or control of industrialequipment (e.g., edge devices 161 a-161 n), assets, processes, and/orevents. The IT applications 164 a-164 n includes network, storage, andcomputing resources for the generation, management, storage, anddelivery of data throughout and between organizations.

The edge gateways 162 a-162 n include devices for facilitatingcommunication between the edge devices 161 a-161 n and the cloud 105 vianetwork 110. For example, the edge gateways 162 a-162 n include one ormore communication interfaces for communicating with the edge devices161 a-161 n and for communicating with the cloud 105 via network 110.According to various embodiments, the communication interfaces of theedge gateways 162 a-162 n include one or more cellular radios,Bluetooth, WiFi, near-field communication radios, Ethernet, or otherappropriate communication devices for transmitting and receivinginformation. According to various embodiments, multiple communicationinterfaces are included in each gateway 162 a-162 n for providingmultiple forms of communication between the edge devices 161 a-161 n,the gateways 162 a-162 n, and the cloud 105 via network 110. Forexample, in one or more embodiments, communication are achieved with theedge devices 161 a-161 n and/or the network 110 through wirelesscommunication (e.g., WiFi, radio communication, etc.) and/or a wireddata connection (e.g., a universal serial bus, an onboard diagnosticsystem, etc.) or other communication modes, such as a local area network(LAN), wide area network (WAN) such as the Internet, atelecommunications network, a data network, or any other type ofnetwork.

According to various embodiments, the edge gateways 162 a-162 n alsoinclude a processor and memory for storing and executing programinstructions to facilitate data processing. For example, in one or moreembodiments, the edge gateways 162 a-162 n are configured to receivedata from the edge devices 161 a-161 n and process the data prior tosending the data to the cloud 105. Accordingly, in one or moreembodiments, the edge gateways 162 a-162 n include one or more softwaremodules or components for providing data processing services and/orother services or methods of the present disclosure. With reference toFIG. 2 , each edge gateway 162 a-162 n includes edge services 165 a-165n and edge connectors 166 a-166 n. According to various embodiments, theedge services 165 a-165 n include hardware and software components forprocessing the data from the edge devices 161 a-161 n. According tovarious embodiments, the edge connectors 166 a-166 n include hardwareand software components for facilitating communication between the edgegateway 162 a-162 n and the cloud 105 via network 110, as detailedabove. In some cases, any of edge devices 161 a-n, edge connectors 166a-n, and edge gateways 162 a-n have their functionality combined,omitted, or separated into any combination of devices. In other words,an edge device and its connector and gateway need not necessarily bediscrete devices.

FIG. 2 illustrates a schematic block diagram of framework 200 of the IoTplatform 125, according to the present disclosure. The IoT platform 125of the present disclosure is a platform for enterprise performancemanagement that uses real-time accurate models and visual analytics todeliver intelligent actionable recommendations and/or analytics forsustained peak performance of the enterprise 160 a-160 n. The IoTplatform 125 is an extensible platform that is portable for deploymentin any cloud or data center environment for providing anenterprise-wide, top to bottom view, displaying the status of processes,assets, people, and safety. Further, the IoT platform 125 supportsend-to-end capability to execute digital twins against process data andto translate the output into actionable insights, using the framework200, detailed further below.

As shown in FIG. 2 , the framework 200 of the IoT platform 125 comprisesa number of layers including, for example, an IoT layer 205, anenterprise integration layer 210, a data pipeline layer 215, a datainsight layer 220, an application services layer 225, and anapplications layer 230. The IoT platform 125 also includes a coreservices layer 235 and an extensible object model (EOM) 250 comprisingone or more knowledge graphs 251. The layers 205-235 further includevarious software components that together form each layer 205-235. Forexample, in one or more embodiments, each layer 205-235 includes one ormore of the modules 141, models 142, engines 143, databases 144,services 145, applications 146, or combinations thereof. In someembodiments, the layers 205-235 are combined to form fewer layers. Insome embodiments, some of the layers 205-235 are separated intoseparate, more numerous layers. In some embodiments, some of the layers205-235 are removed while others may be added.

The IoT platform 125 is a model-driven architecture. Thus, theextensible object model 250 communicates with each layer 205-230 tocontextualize site data of the enterprise 160 a-160 n using anextensible graph based object model (or “asset model”). In one or moreembodiments, the extensible object model 250 is associated withknowledge graphs 251 where the equipment (e.g., edge devices 161 a-161n) and processes of the enterprise 160 a-160 n are modeled. Theknowledge graphs 251 of EOM 250 are configured to store the models in acentral location. The knowledge graphs 251 define a collection of nodesand links that describe real-world connections that enable smartsystems. As used herein, a knowledge graph 251: (i) describes real-worldentities (e.g., edge devices 161 a-161 n) and their interrelationsorganized in a graphical interface; (ii) defines possible classes andrelations of entities in a schema; (iii) enables interrelating arbitraryentities with each other; and (iv) covers various topical domains. Inother words, the knowledge graphs 251 define large networks of entities(e.g., edge devices 161 a-161 n), semantic types of the entities,properties of the entities, and relationships between the entities.Thus, the knowledge graphs 251 describe a network of “things” that arerelevant to a specific domain or to an enterprise or organization.Knowledge graphs 251 are not limited to abstract concepts and relations,but can also contain instances of objects, such as, for example,documents and datasets. In some embodiments, the knowledge graphs 251include resource description framework (RDF) graphs. As used herein, a“RDF graph” is a graph data model that formally describes the semantics,or meaning, of information. The RDF graph also represents metadata(e.g., data that describes data). According to various embodiments,knowledge graphs 251 also include a semantic object model. The semanticobject model is a subset of a knowledge graph 251 that defines semanticsfor the knowledge graph 251. For example, the semantic object modeldefines the schema for the knowledge graph 251.

As used herein, EOM 250 includes a collection of application programminginterfaces (APIs) that enables seeded semantic object models to beextended. For example, the EOM 250 of the present disclosure enables acustomer's knowledge graph 251 to be built subject to constraintsexpressed in the customer's semantic object model. Thus, the knowledgegraphs 251 are generated by customers (e.g., enterprises ororganizations) to create models of the edge devices 161 a-161 n of anenterprise 160 a-160 n, and the knowledge graphs 251 are input into theEOM 250 for visualizing the models (e.g., the nodes and links).

The models describe the assets (e.g., the nodes) of an enterprise (e.g.,the edge devices 161 a-161 n) and describe the relationship of theassets with other components (e.g., the links). The models also describethe schema (e.g., describe what the data is), and therefore the modelsare self-validating. For example, in one or more embodiments, the modeldescribes the type of sensors mounted on any given asset (e.g., edgedevice 161 a-161 n) and the type of data that is being sensed by eachsensor. According to various embodiments, a KPI framework is used tobind properties of the assets in the extensible object model 250 toinputs of the KPI framework. Accordingly, the IoT platform 125 is anextensible, model-driven end-to-end stack including: two-way model syncand secure data exchange between the edge 115 and the cloud 105,metadata driven data processing (e.g., rules, calculations, andaggregations), and model driven visualizations and applications. As usedherein, “extensible” refers to the ability to extend a data model toinclude new properties/columns/fields, new classes/tables, and newrelations. Thus, the IoT platform 125 is extensible with regards to edgedevices 161 a-161 n and the applications 146 that handle those devices161 a-161 n. For example, when new edge devices 161 a-161 n are added toan enterprise 160 a-160 n system, the new devices 161 a-161 n willautomatically appear in the IoT platform 125 so that the correspondingapplications 146 understand and use the data from the new devices 161a-161 n.

In some cases, asset templates are used to facilitate configuration ofinstances of edge devices 161 a-161 n in the model using commonstructures. An asset template defines the typical properties for theedge devices 161 a-161 n of a given enterprise 160 a-160 n for a certaintype of device. For example, an asset template of a pump includesmodeling the pump having inlet and outlet pressures, speed, flow, etc.The templates may also include hierarchical or derived types of edgedevices 161 a-161 n to accommodate variations of a base type of device161 a-161 n. For example, a reciprocating pump is a specialization of abase pump type and would include additional properties in the template.Instances of the edge device 161 a-161 n in the model are configured tomatch the actual, physical devices of the enterprise 160 a-160 n usingthe templates to define expected attributes of the device 161 a-161 n.Each attribute is configured either as a static value (e.g., capacity is1000 BPH) or with a reference to a time series tag that provides thevalue. The knowledge graph 251 can automatically map the tag to theattribute based on naming conventions, parsing, and matching the tag andattribute descriptions and/or by comparing the behavior of the timeseries data with expected behavior. In one or more embodiments, each ofthe key attribute contributing to one or more metrics to drive adashboard is marked with one or more metric tags such that a dashboardvisualization is generated.

The modeling phase includes an onboarding process for syncing the modelsbetween the edge 115 and the cloud 105. For example, in one or moreembodiments, the onboarding process includes a simple onboardingprocess, a complex onboarding process, and/or a standardized rolloutprocess. The simple onboarding process includes the knowledge graph 251receiving raw model data from the edge 115 and running context discoveryalgorithms to generate the model. The context discovery algorithms readthe context of the edge naming conventions of the edge devices 161 a-161n and determine what the naming conventions refer to. For example, inone or more embodiments, the knowledge graph 251 receives “TMP” duringthe modeling phase and determine that “TMP” relates to “temperature.”The generated models are then published. The complex onboarding processincludes the knowledge graph 251 receiving the raw model data, receivingpoint history data, and receiving site survey data. According to variousembodiments, the knowledge graph 251 then uses these inputs to run thecontext discovery algorithms. According to various embodiments, thegenerated models are edited and then the models are published. Thestandardized rollout process includes manually defining standard modelsin the cloud 105 and pushing the models to the edge 115.

The IoT layer 205 includes one or more components for device management,data ingest, and/or command/control of the edge devices 161 a-161 n. Thecomponents of the IoT layer 205 enable data to be ingested into, orotherwise received at, the IoT platform 125 from a variety of sources.For example, in one or more embodiments, data is ingested from the edgedevices 161 a-161 n through process historians or laboratory informationmanagement systems. The IoT layer 205 is in communication with the edgeconnectors 166 a-166 n installed on the edge gateways 162 a-162 nthrough network 110, and the edge connectors 166 a-166 n send the datasecurely to the IoT platform 205. In some embodiments, only authorizeddata is sent to the IoT platform 125, and the IoT platform 125 onlyaccepts data from authorized edge gateways 162 a-162 n and/or edgedevices 161 a-161 n. According to various embodiments, data is sent fromthe edge gateways 162 a-162 n to the IoT platform 125 via directstreaming and/or via batch delivery. Further, after any network orsystem outage, data transfer will resume once communication isre-established and any data missed during the outage will be backfilledfrom the source system or from a cache of the IoT platform 125.According to various embodiments, the IoT layer 205 also includescomponents for accessing time series, alarms and events, andtransactional data via a variety of protocols.

The enterprise integration layer 210 includes one or more components forevents/messaging, file upload, and/or REST/OData. The components of theenterprise integration layer 210 enable the IoT platform 125 tocommunicate with third party cloud applications 211, such as anyapplication(s) operated by an enterprise in relation to its edgedevices. For example, the enterprise integration layer 210 connects withenterprise databases, such as guest databases, customer databases,financial databases, patient databases, etc. The enterprise integrationlayer 210 provides a standard application programming interface (API) tothird parties for accessing the IoT platform 125. The enterpriseintegration layer 210 also enables the IoT platform 125 to communicatewith the OT systems 163 a-163 n and IT applications 164 a-164 n of theenterprise 160 a-160 n. Thus, the enterprise integration layer 210enables the IoT platform 125 to receive data from the third-partyapplications 211 rather than, or in combination with, receiving the datafrom the edge devices 161 a-161 n directly.

The data pipeline layer 215 includes one or more components for datacleansing/enriching, data transformation, datacalculations/aggregations, and/or API for data streams. Accordingly, inone or more embodiments, the data pipeline layer 215 pre-processesand/or performs initial analytics on the received data. The datapipeline layer 215 executes advanced data cleansing routines including,for example, data correction, mass balance reconciliation, dataconditioning, component balancing and simulation to ensure the desiredinformation is used as a basis for further processing. The data pipelinelayer 215 also provides advanced and fast computation. For example,cleansed data is run through enterprise-specific digital twins.According to various embodiments, the enterprise-specific digital twinsinclude a reliability advisor containing process models to determine thecurrent operation and the fault models to trigger any early detectionand determine an appropriate resolution. According to variousembodiments, the digital twins also include an optimization advisor thatintegrates real-time economic data with real-time process data, selectsthe right feed for a process, and determines optimal process conditionsand product yields.

According to various embodiments, the data pipeline layer 215 employsmodels and templates to define calculations and analytics. Additionallyor alternatively, according to various embodiments, the data pipelinelayer 215 employs models and templates to define how the calculationsand analytics relate to the assets (e.g., the edge devices 161 a-161 n).For example, in an embodiment, a pump template defines pump efficiencycalculations such that every time a pump is configured, the standardefficiency calculation is automatically executed for the pump. Thecalculation model defines the various types of calculations, the type ofengine that should run the calculations, the input and outputparameters, the preprocessing requirement and prerequisites, theschedule, etc. According to various embodiments, the actual calculationor analytic logic is defined in the template or it may be referenced.Thus, according to various embodiments, the calculation model isemployed to describe and control the execution of a variety of differentprocess models. According to various embodiments, calculation templatesare linked with the asset templates such that when an asset (e.g., edgedevice 161 a-161 n) instance is created, any associated calculationinstances are also created with their input and output parameters linkedto the appropriate attributes of the asset (e.g., edge device 161 a-161n).

According to various embodiments, the IoT platform 125 supports avariety of different analytics models including, for example, firstprinciples models, empirical models, engineered models, user-definedmodels, machine learning models, built-in functions, and/or any othertypes of analytics models. Fault models and predictive maintenancemodels will now be described by way of example, but any type of modelsmay be applicable.

Fault models are used to compare current and predicted enterprise 160a-160 n performance to identify issues or opportunities, and thepotential causes or drivers of the issues or opportunities. The IoTplatform 125 includes rich hierarchical symptom-fault models to identifyabnormal conditions and their potential consequences. For example, inone or more embodiments, the IoT platform 125 drill downs from ahigh-level condition to understand the contributing factors, as well asdetermining the potential impact a lower level condition may have. Theremay be multiple fault models for a given enterprise 160 a-160 n lookingat different aspects such as process, equipment, control, and/oroperations. According to various embodiments, each fault modelidentifies issues and opportunities in their domain, and can also lookat the same core problem from a different perspective. According tovarious embodiments, an overall fault model is layered on top tosynthesize the different perspectives from each fault model into anoverall assessment of the situation and point to the true root cause.

According to various embodiments, when a fault or opportunity isidentified, the IoT platform 125 provides recommendations about anoptimal corrective action to take. Initially, the recommendations arebased on expert knowledge that has been preprogrammed into the system byprocess and equipment experts. A recommendation services module presentsthis information in a consistent way regardless of source, and supportsworkflows to track, close out, and document the recommendationfollow-up. According to various embodiments, the recommendationfollow-up is employed to improve the overall knowledge of the systemover time as existing recommendations are validated (or not) or newcause and effect relationships are learned by users and/or analytics.

According to various embodiments, the models are used to accuratelypredict what will occur before it occurs and interpret the status of theinstalled base. Thus, the IoT platform 125 enables operators to quicklyinitiate maintenance measures when irregularities occur. According tovarious embodiments, the digital twin architecture of the IoT platform125 employs a variety of modeling techniques. According to variousembodiments, the modeling techniques include, for example, rigorousmodels, fault detection and diagnostics (FDD), descriptive models,predictive maintenance, prescriptive maintenance, process optimization,and/or any other modeling technique.

According to various embodiments, the rigorous models are converted fromprocess design simulation. In this manner, process design is integratedwith feed conditions and production requirement. Process changes andtechnology improvement provide business opportunities that enable moreeffective maintenance schedule and deployment of resources in thecontext of production needs. The fault detection and diagnostics includegeneralized rule sets that are specified based on industry experienceand domain knowledge and can be easily incorporated and used workingtogether with equipment models. According to various embodiments, thedescriptive models identifies a problem and the predictive modelsdetermines possible damage levels and maintenance options. According tovarious embodiments, the descriptive models include models for definingthe operating windows for the edge devices 161 a-161 n.

Predictive maintenance includes predictive analytics models developedbased on rigorous models and statistic models, such as, for example,principal component analysis (PCA) and partial least square (PLS).According to various embodiments, machine learning methods are appliedto train models for fault prediction. According to various embodiments,predictive maintenance leverages FDD-based algorithms to continuouslymonitor individual control and equipment performance. Predictivemodeling is then applied to a selected condition indicator thatdeteriorates in time. Prescriptive maintenance includes determining anoptimal maintenance option and when it should be performed based onactual conditions rather than time-based maintenance schedule. Accordingto various embodiments, prescriptive analysis selects the right solutionbased on the company's capital, operational, and/or other requirements.Process optimization is determining optimal conditions via adjustingset-points and schedules. The optimized set-points and schedules can becommunicated directly to the underlying controllers, which enablesautomated closing of the loop from analytics to control.

The data insight layer 220 includes one or more components for timeseries databases (TDSB), relational/document databases, data lakes,blob, files, images, and videos, and/or an API for data query. Accordingto various embodiments, when raw data is received at the IoT platform125, the raw data is stored as time series tags or events in warmstorage (e.g., in a TSDB) to support interactive queries and to coldstorage for archive purposes. According to various embodiments, data issent to the data lakes for offline analytics development. According tovarious embodiments, the data pipeline layer 215 accesses the datastored in the databases of the data insight layer 220 to performanalytics, as detailed above.

The application services layer 225 includes one or more components forrules engines, workflow/notifications, KPI framework, insights (e.g.,actionable insights), decisions, recommendations, machine learning,and/or an API for application services. The application services layer225 enables building of applications 146 a-d. The applications layer 230includes one or more applications 146 a-d of the IoT platform 125. Forexample, according to various embodiments, the applications 146 a-dincludes a buildings application 146 a, a plants application 146 b, anaero application 146 c, and other enterprise applications 146 d.According to various embodiments, the applications 146 includes generalapplications 146 for portfolio management, asset management, autonomouscontrol, and/or any other custom applications. According to variousembodiments, portfolio management includes the KPI framework and aflexible user interface (UI) builder. According to various embodiments,asset management includes asset performance and asset health. Accordingto various embodiments, autonomous control includes energy optimizationand/or predictive maintenance. As detailed above, according to variousembodiments, the general applications 146 is extensible such that eachapplication 146 is configurable for the different types of enterprises160 a-160 n (e.g., buildings application 146 a, plants application 146b, aero application 146 c, and other enterprise applications 146 d).

The applications layer 230 also enables visualization of performance ofthe enterprise 160 a-160 n. For example, dashboards provide a high-leveloverview with drill downs to support deeper investigations.Recommendation summaries give users prioritized actions to addresscurrent or potential issues and opportunities. Data analysis toolssupport ad hoc data exploration to assist in troubleshooting and processimprovement.

The core services layer 235 includes one or more services of the IoTplatform 125. According to various embodiments, the core services 235include data visualization, data analytics tools, security, scaling, andmonitoring. According to various embodiments, the core services 235 alsoinclude services for tenant provisioning, single login/common portal,self-service admin, UI library/UI tiles, identity/access/entitlements,logging/monitoring, usage metering, API gateway/dev portal, and the IoTplatform 125 streams.

FIG. 3 illustrates a system 300 that provides an exemplary environmentaccording to one or more described features of one or more embodimentsof the disclosure. According to an embodiment, the system 300 includesan enterprise data management computer system 302 to facilitate apractical application of data analytics technology and/or digitaltransformation technology to provide optimization related to enterprisedata management. In one or more embodiments, the enterprise datamanagement computer system 302 facilitates a practical application ofknowledge graph generation and/or contextualization of data for aknowledge graph to provide optimization related to enterprise datamanagement. In one or more embodiments, the enterprise data managementcomputer system 302 stores and/or analyzes data that is aggregated fromone or more assets and/or one or more data sources associated with anenterprise system (e.g., a building system, an industrial system oranother type of enterprise system). In one or more embodiments, theenterprise data management computer system 302 employs artificialintelligence to provide a knowledge graph and/or contextualization ofdata for a knowledge graph. In one or more embodiments, the enterprisedata management computer system 302 generates a dashboard visualization(e.g., an enterprise data management dashboard) based on the knowledgegraph and/or one or more insights obtained based on the knowledge graph.

In an embodiment, the enterprise data management computer system 302 isa server system (e.g., a server device) that facilitates a dataanalytics platform between one or more computing devices, one or moredata sources, and/or one or more assets. In one or more embodiments, theenterprise data management computer system 302 is a device with one ormore processors and a memory. In one or more embodiments, the enterprisedata management computer system 302 is a computer system from thecomputer systems 120. For example, in one or more embodiments, theenterprise data management computer system 302 is implemented via thecloud 105. The enterprise data management computer system 302 is alsorelated to one or more technologies, such as, for example, enterprisetechnologies, connected building technologies, industrial technologies,Internet of Things (IoT) technologies, data analytics technologies,digital transformation technologies, cloud computing technologies, clouddatabase technologies, server technologies, network technologies,private enterprise network technologies, wireless communicationtechnologies, machine learning technologies, artificial intelligencetechnologies, digital processing technologies, electronic devicetechnologies, computer technologies, supply chain analyticstechnologies, aircraft technologies, industrial technologies,cybersecurity technologies, navigation technologies, asset visualizationtechnologies, oil and gas technologies, petrochemical technologies,refinery technologies, process plant technologies, procurementtechnologies, and/or one or more other technologies.

Moreover, the enterprise data management computer system 302 provides animprovement to one or more technologies such as enterprise technologies,connected building technologies, industrial technologies, IoTtechnologies, data analytics technologies, digital transformationtechnologies, cloud computing technologies, cloud database technologies,server technologies, network technologies, private enterprise networktechnologies, wireless communication technologies, machine learningtechnologies, artificial intelligence technologies, digital processingtechnologies, electronic device technologies, computer technologies,supply chain analytics technologies, aircraft technologies, industrialtechnologies, cybersecurity technologies, navigation technologies, assetvisualization technologies, oil and gas technologies, petrochemicaltechnologies, refinery technologies, process plant technologies,procurement technologies, and/or one or more other technologies. In animplementation, the enterprise data management computer system 302improves performance of a computing device. For example, in one or moreembodiments, the enterprise data management computer system 302 improvesprocessing efficiency of a computing device (e.g., a server), reducespower consumption of a computing device (e.g., a server), improvesquality of data provided by a computing device (e.g., a server), etc.

The enterprise data management computer system 302 includes a dataaggregation component 304, a knowledge graph component 306, an actioncomponent 308, an operational limit component 338, and/or a dashboardvisualization component 348. Additionally, in one or more embodiments,the enterprise data management computer system 302 includes a processor310 and/or a memory 312. In certain embodiments, one or more aspects ofthe enterprise data management computer system 302 (and/or othersystems, apparatuses and/or processes disclosed herein) constituteexecutable instructions embodied within a computer-readable storagemedium (e.g., the memory 312). For instance, in an embodiment, thememory 312 stores computer executable component and/or executableinstructions (e.g., program instructions). Furthermore, the processor310 facilitates execution of the computer executable components and/orthe executable instructions (e.g., the program instructions). In anexample embodiment, the processor 310 is configured to executeinstructions stored in the memory 312 or otherwise accessible to theprocessor 310.

The processor 310 is a hardware entity (e.g., physically embodied incircuitry) capable of performing operations according to one or moreembodiments of the disclosure. Alternatively, in an embodiment where theprocessor 310 is embodied as an executor of software instructions, thesoftware instructions configure the processor 310 to perform one or morealgorithms and/or operations described herein in response to thesoftware instructions being executed. In an embodiment, the processor310 is a single core processor, a multi-core processor, multipleprocessors internal to the enterprise data management computer system302, a remote processor (e.g., a processor implemented on a server),and/or a virtual machine. In certain embodiments, the processor 310 isin communication with the memory 312, the data aggregation component304, the knowledge graph component 306, the action component 308, theoperational limit component 338, and/or the dashboard visualizationcomponent 348 via a bus to, for example, facilitate transmission of dataamong the processor 310, the memory 312, the data aggregation component304, the knowledge graph component 306, the action component 308, theoperational limit component 338, and/or the dashboard visualizationcomponent 348. The processor 310 may be embodied in a number ofdifferent ways and, in certain embodiments, includes one or moreprocessing devices configured to perform independently. Additionally oralternatively, in one or more embodiments, the processor 310 includesone or more processors configured in tandem via a bus to enableindependent execution of instructions, pipelining of data, and/ormulti-thread execution of instructions.

The memory 312 is non-transitory and includes, for example, one or morevolatile memories and/or one or more non-volatile memories. In otherwords, in one or more embodiments, the memory 312 is an electronicstorage device (e.g., a computer-readable storage medium). The memory312 is configured to store information, data, content, one or moreapplications, one or more instructions, or the like, to enable theenterprise data management computer system 302 to carry out variousfunctions in accordance with one or more embodiments disclosed herein.As used herein in this disclosure, the term “component,” “system,” andthe like, is a computer-related entity. For instance, “a component,” “asystem,” and the like disclosed herein is either hardware, software, ora combination of hardware and software. As an example, a component is,but is not limited to, a process executed on a processor, a processor,circuitry, an executable component, a thread of instructions, a program,and/or a computer entity.

In an embodiment, the enterprise data management computer system 302(e.g., the data aggregation component 304 of the enterprise datamanagement computer system 302) receives asset data from the edgedevices 161 a-161 n. In one or more embodiments, the edge devices 161a-161 n are associated with a portfolio of assets. For instance, in oneor more embodiments, the edge devices 161 a-161 n include one or moreassets in a portfolio of assets. The edge devices 161 a-161 n include,in one or more embodiments, one or more databases, one or more assets(e.g., one or more building assets, one or more industrial assets,etc.), one or more IoT devices (e.g., one or more industrial IoTdevices), one or more industrial assets, one or more connected buildingassets, one or more sensors, one or more actuators, one or moreprocessors, one or more computers, one or more valves, one or more pumps(e.g., one or more centrifugal pumps, etc.), one or more motors, one ormore compressors, one or more turbines, one or more ducts, one or moreheaters, one or more chillers, one or more coolers, one or more boilers,one or more furnaces, one or more heat exchangers, one or more fans, oneor more blowers, one or more conveyor belts, one or more vehiclecomponents, one or more cameras, one or more displays, one or moresecurity components, one or more HVAC components, industrial equipment,factory equipment, and/or one or more other devices that are connectedto the network 110 for collecting, sending, and/or receivinginformation. In one or more embodiments, the edge device 161 a-161 ninclude, or is otherwise in communication with, one or more controllersfor selectively controlling a respective edge device 161 a-161 n and/orfor sending/receiving information between the edge devices 161 a-161 nand the enterprise data management computer system 302 via the network110. The asset data includes, for example, industrial data, processdata, operational technology data, alarm limits, planning limits,quality limits, environment limits, connected building data, sensordata, real-time data, historical data, event data, location data, and/orother data associated with the edge devices 161 a-161 n.

In certain embodiments, at least one edge device from the edge devices161 a-161 n incorporates encryption capabilities to facilitateencryption of one or more portions of the asset data 314. Additionally,in one or more embodiments, the enterprise data management computersystem 302 (e.g., the data aggregation component 304 of the enterprisedata management computer system 302) receives the asset data 314 via thenetwork 110. In one or more embodiments, the network 110 is a Wi-Finetwork, a Near Field Communications (NFC) network, a WorldwideInteroperability for Microwave Access (WiMAX) network, a personal areanetwork (PAN), a short-range wireless network (e.g., a Bluetooth®network), an infrared wireless (e.g., IrDA) network, an ultra-wideband(UWB) network, an induction wireless transmission network, and/oranother type of network. In one or more embodiments, the edge devices161 a-161 n are associated with an industrial environment (e.g., aplant, etc.). Additionally or alternatively, in one or more embodiments,the edge devices 161 a-161 n are associated with components of the edge115 such as, for example, one or more enterprises 160 a-160 n.

In one or more embodiments, the data aggregation component 304aggregates the asset data from the edge devices 161 a-161 n. Forinstance, in one or more embodiments, the data aggregation component 304aggregates the asset data into one or more databases and/or one or moredata repositories. In one or more embodiments, the data aggregationcomponent 304 is in communication with and/or controls one or moreportions of one or more operational technology systems 318. The one ormore operational technology systems 318 include, for example, processdata historian 321, limit repository 323, operation monitoring 324,and/or alarm management 326 related to the edge devices 161 a-161 n. Theprocess data historian 321 is a system that collects and/or storestime-series data related to the edge devices 161 a-161 n. For example,in one or more embodiments, the process data historian 321 storesprocess data related to one or more processes executed by the edgedevices 161 a-161 n. The limit repository 323 is a system that managesand/or stores limit values (e.g., threshold values) for alarms,planning, quality, and/or environment criteria associated with the edgedevices 161 a-161 n. The limit values are, for example, boundaries,constraints, and/or operating limits associated with process control ofthe edge devices 161 a-161 n. In one or more embodiments, the limitrepository 323 monitors and/or maintains consistency among variousprocesses performed by the edge devices 161 a-161 n. The operationmonitoring 324 monitors one or more processes performed by the edgedevices 161 a-161 n. In one or more embodiments, the operationmonitoring 324 employs performance metrics to monitor performance of oneor more processes performed by the edge devices 161 a-161 n. In one ormore embodiments, the operation monitoring 324 repeatedly monitors oneor more processes performed by the edge devices 161 a-161 n based onpredetermined intervals of time. In one or more embodiments, theoperation monitoring 324 measures and/or calculates process data andcompares the process data against one or more performance metrics forthe process data. The alarm management 326 manages one or more alarmsrelated to one or more processes performed by the edge devices 161 a-161n. In one or more embodiments, the alarm management 326 optimizes one ormore alarms related to one or more processes performed by the edgedevices 161 a-161 n. In one or more embodiments, the alarm management326 manages thresholds and/or other criteria for respective alarmsrelated to one or more processes performed by the edge devices 161 a-161n. In one or more embodiments, the alarm management 326 manages thealarms based on performance metrics associated with the one or moreprocesses performed by the edge devices 161 a-161 n.

In one or more embodiments, the data aggregation component 304repeatedly updates data for the one or more operational technologysystems 318 based on data provided by the edge devices 161 a-161 n. Forinstance, in one or more embodiments, the data aggregation component 304stores new data and/or modified data associated with the edge devices161 a-161 n. In one or more embodiments, the data aggregation component304 repeatedly scans the edge devices 161 a-161 n to determine new datafor storage. In one or more embodiments, the data aggregation component304 formats one or more portions of data associated with the edgedevices 161 a-161 n. For instance, in one or more embodiments, the dataaggregation component 304 provides a formatted version of dataassociated with the edge devices 161 a-161 n to the one or moreoperational technology systems 318. In an embodiment, the formattedversion of the data associated with the edge devices 161 a-161 n isformatted with one or more defined formats associated with the processdata historian 321, the limit repository 323, the operation monitoring324, and/or the alarm management 326. A defined format is, for example,a structure for data fields and/or data files associated with theprocess data historian 321, the limit repository 323, the operationmonitoring 324, and/or the alarm management 326.

In one or more embodiments, the data aggregation component 304identifies and/or groups data types for data associated with the edgedevices 161 a-161 n. In one or more embodiments, the data aggregationcomponent 304 employs batching, concatenation of data, identification ofdata types, merging of data, grouping of data, reading of data and/orwriting of data associated with the edge devices 161 a-161 n tofacilitate storage of data for the process data historian 321, the limitrepository 323, the operation monitoring 324, and/or the alarmmanagement 326. In one or more embodiments, the data aggregationcomponent 304 groups data associated with the edge devices 161 a-161 nbased on corresponding features and/or attributes of the data. In one ormore embodiments, the data aggregation component 304 groups dataassociated with the edge devices 161 a-161 n based on correspondingidentifiers (e.g., a matching asset hierarchy level, a matching asset, amatching connected building, etc.) for the data. In one or moreembodiments, the data aggregation component 304 employs one or morelocality-sensitive hashing techniques to group data associated with theedge devices 161 a-161 n based on similarity scores and/or calculateddistances between different data.

The knowledge graph component 306 manages a knowledge graph 316 relatedto the one or more assets. The knowledge graph 316 is a knowledge graphdata structure configured for the one or more assets. For example, inone or more embodiments, the knowledge graph 316 is a semantic networkgraph that represents a network of data and/or interconnectedrelationships associated with the edge devices 161 a-161 n and/or one ormore systems related to the edge devices 161 a-161 n. In one or moreembodiments, the knowledge graph 316 corresponds to the knowledge graph251. In an embodiment, the knowledge graph 316 is an industrialknowledge graph related to one or more industrial assets. In one or moreembodiments, the knowledge graph is configured as an extensible objectmodel associated with the edge devices 161 a-161 n and/or one or moresystems related to the edge devices 161 a-161 n. In one or moreembodiments, the knowledge graph 316 includes a set of nodes and/or aset of connectors that graphically represents data associated with theprocess data historian 321, the limit repository 323, the operationmonitoring 324, and/or the alarm management 326. Furthermore, in one ormore embodiments, the knowledge graph 316 graphically representsrelationships between the data associated with the process datahistorian 321, the limit repository 323, the operation monitoring 324,and/or the alarm management 326. In certain embodiments, the knowledgegraph 316 graphically represents patterns and/or trends between the dataassociated with the process data historian 321, the limit repository323, the operation monitoring 324, and/or the alarm management 326. Inone or more embodiments, the knowledge graph component 306contextualizes the data associated with the process data historian 321,the limit repository 323, the operation monitoring 324, and/or the alarmmanagement 326 based on attributes for the data associated with theprocess data historian 321, the limit repository 323, the operationmonitoring 324, and/or the alarm management 326. Additionally oralternatively, in one or more embodiments, the knowledge graph component306 allocates the data within the knowledge graph 316 based on theattributes for the data.

In one or more embodiments, the enterprise data management computersystem 302 (e.g., the knowledge graph component 306 of the enterprisedata management computer system 302) receives a request 320. In anembodiment, the request 320 is a request to generate knowledge graphdata related to one or more assets. For instance, in one or moreembodiments, the request 320 is a request to generate knowledge graphdata related to the edge devices 161 a-161 n. In one or moreembodiments, the request 320 is a request to generate knowledge graphdata for the knowledge graph 316. In another embodiment, the request 320is a request to obtain one or more insights related to one or moreassets. For instance, in one or more embodiments, the request 320 is arequest to obtain one or more insights related to the edge devices 161a-161 n. In one or more embodiments, the request 320 is a request toobtain one or more insights with respect to the knowledge graph 316. Inanother embodiment, the request 320 is a request to generate a dashboardvisualization related to one or more assets. For instance, in one ormore embodiments, the request 320 is a request to generate a dashboardvisualization related to the edge devices 161 a-161 n. In one or moreembodiments, the request 320 is a request to generate a dashboardvisualization related to the knowledge graph 316.

In one or more embodiments, the request 320 includes one or more assetdescriptors that describe the one or more assets. For instance, in oneor more embodiments, the request 320 includes one or more assetdescriptors that describe the edge devices 161 a-161 n. An assetdescriptor includes, for example, an asset name, an asset identifier, anasset level and/or other information associated with an asset.Additionally or alternatively, in one or more embodiments, the request320 includes one or more user identifiers describing a user role for auser associated with the request 320 and/or access of a dashboardvisualization. A user identifier includes, for example, an identifierfor a user role name (e.g., a manager, an executive, a maintenanceengineer, a process engineer, etc.). Additionally or alternatively, inone or more embodiments, the request 320 includes one or more metricscontext identifiers describing context for the knowledge graph 316. Ametrics context identifier includes, for example, an identifier for aplant performance metric, an asset performance metric, a goal (e.g.,review production related to one or more assets, etc.). Additionally oralternatively, in one or more embodiments, the request 320 includes oneor more time interval identifier describing an interval of time for oneor more insights related to the one or more assets. In one or moreembodiments, a time interval identifier is a reporting time identifierdescribing an interval of time for one or more insights related to theone or more assets. In an embodiment, the request 320 is generated by acomputing device (e.g., a user computing device). For example, in anembodiment, the request 320 is generated via a visual display (e.g., adashboard visualization) of a computing device. In another example, therequest 320 is voice input generated via a microphone of a computingdevice. In another embodiment, the request 320 is generated by acontroller associated with an asset (e.g., the edge devices 161 a-161n). In another embodiment, the request 320 is generated by the processor310. For example, in another embodiment, the request 320 is generatedbased on a schedule for updating the knowledge graph 316.

In one or more embodiments, in response to the request 320, theknowledge graph component 306 obtains aggregated operational technologydata from one or more data sources associated with the one or moreassets. The one or more data sources includes, for example, the processdata historian 321, the limit repository 323, the operation monitoring324, and/or the alarm management 326. For example, in one or moreembodiments, the knowledge graph component 306 obtains the aggregatedoperational technology data from the process data historian 321, thelimit repository 323, the operation monitoring 324, and/or the alarmmanagement 326. In one or more embodiments, the knowledge graphcomponent 306 obtains one or more portions of the aggregated operationaltechnology data based on the one or more asset descriptors, the useridentifier, the one or more metrics context identifiers, and/or the oneor more time interval identifiers.

Additionally, in response to the request 320, the knowledge graphcomponent 306 contextualizes the aggregated operational technology datato generate the knowledge graph data for the knowledge graph 316. In oneor more embodiments, the knowledge graph component 306 contextualizesthe aggregated operational technology data based on configuration datafor the one or assets and/or one or more systems associated with the oneor more assets. For example, in one or more embodiments, the knowledgegraph component 306 contextualizes the aggregated operational technologydata based on configuration data for the edge devices 161 a-161 n and/orthe one or more enterprises 160 a-160 n that includes the edge devices161 a-161 n. In an embodiment, the configuration data includesconfiguration information of an industrial plant system that includesone or more industrial assets. In one or more embodiments, theconfiguration data includes configuration data related to processthresholds, process limits, process targets, process tolerances, alarmcriteria, planning criteria, quality criteria, environment criteria,manufacturing execution services, operations monitoring, assetperformance monitoring, distributed control systems, alarm management,plant historians, integrity operating windows, assets tags, virtualprocesses for an asset, network layers, process characterizations,process measurements, process controls,

Additionally or alternatively, in one or more embodiments, the knowledgegraph component 306 contextualizes the aggregated operational technologydata based on a set of contextualization rules for the one or more datasources. For example, in one or more embodiments, the set ofcontextualization rules for the one or more data sources includes rulesfor one or more tags, one or more labels, one or more attributes, and/orone or more other classifications to contextualize the aggregatedoperational technology data. In one or more embodiments, the set ofcontextualization rules for the one or more data sources includes rulesfor one or more weights to determine one or more tags, one or morelabels, one or more attributes, and/or one or more other classificationsto contextualize the aggregated operational technology data. In one ormore embodiments, the set of contextualization rules are a set ofcontextualization rules for one or more data formats associated with theone or more data sources. For example, in one or more embodiments, theset of contextualization rules includes respective parsing rules and/orclassification rules for one or more data formats. In an embodiment, adata format is a data format for a data file that includes operationaltechnology data associated with the process data historian 321, thelimit repository 323, the operation monitoring 324, and/or the alarmmanagement 326. In certain embodiments, the knowledge graph component306 parses alarm limit data, planning limit data, quality limit data,environment limit data, or other limit data for the one or more assetsbased on the configuration data for the one or assets and/or the set ofcontextualization rules for the one or more data sources. In certainembodiments, the knowledge graph component 306 parses operationdeviation data associated with monitoring of the one or more assetsbased on the configuration data for the one or assets and/or the set ofcontextualization rules for the one or more data sources. In certainembodiments, the knowledge graph component 306 parses alarm history dataassociated with the one or more assets based on the configuration datafor the one or assets and/or the set of contextualization rules for theone or more data sources.

In one or more embodiments, the set of contextualization rules for theone or more data sources identifies a type of contextualizationtechnique to employ for the respective data sources. In one or moreembodiments, the knowledge graph component 306 parses the aggregatedoperational technology data based on one or more parameters associatedwith a contextualization technique associated with the set ofcontextualization rules. A type of contextualization technique includes,in one or more embodiments, a knowledge graph embedding technique, aknowledge representation learning technique, a fuzzy matching technique,a clustering technique, a statistical relational learning technique, amachine learning technique, a deep learning technique, and/or anothertype of contextualization technique. In one or more embodiments, theknowledge graph component 306 employs one or more machine learningtechniques to determine the one or more tags, one or more labels, one ormore attributes, and/or one or more other classifications tocontextualize the aggregated operational technology data. In anembodiment, the knowledge graph component 306 performs a fuzzy matchingtechnique with respect to the aggregated operational technology data todetermine the one or more tags, one or more labels, one or moreattributes, and/or one or more other classifications to contextualizethe aggregated operational technology data. In one or more embodiments,the knowledge graph component 306 contextualizes the aggregatedoperational technology data based on the user identifier. For example,in an embodiment, the knowledge graph component 306 contextualizes theaggregated operational technology data based on a set ofcontextualization rules configured for the user identifier. In certainembodiments, the knowledge graph component 306 parses the aggregatedoperational technology data based on metadata associated with the useridentifier. Additionally or alternatively, in certain embodiments, theknowledge graph component 306 parses the aggregated operationaltechnology data based on metadata associated with the one or moreassets. In certain embodiments, the knowledge graph component 306selects the knowledge graph 316 from a set of knowledge graphs based onthe user identifier.

Additionally, the knowledge graph component 306 allocates the knowledgegraph data within the knowledge graph 316. In one or more embodiments,the knowledge graph component 306 employs a recurrent neural network tomap data into the knowledge graph 316. In certain embodiments, theknowledge graph component 306 organizes the knowledge graph data basedon an ontological data structure that captures relationships amongrespective knowledge graph data portions within the knowledge graph 316.In certain embodiments, the knowledge graph component 306 allocates theknowledge graph data within the knowledge graph 316 based on the useridentifier.

In one or more embodiments, the knowledge graph component 306additionally or alternatively obtains one or more insights with respectto the knowledge graph data allocated within the knowledge graph 316.For example, in one or more embodiments, the knowledge graph component306 correlates aspects of aggregated operational technology data withinthe knowledge graph 318 to provide the one or more insights. In one ormore embodiments, the knowledge graph component 306 correlates aspectsof aggregated operational technology data within the knowledge graph 318based on the one or more asset descriptors, the user identifier, the oneor more metrics context identifiers, and/or the one or more timeinterval identifiers. In certain embodiments, the aspects of theaggregated operational technology data correlated by the knowledge graphcomponent 306 includes features and/or attributes of the of aggregatedoperational technology data. In one or more embodiments, the knowledgegraph component 306 performs a machine learning process (e.g., a deeplearning process) to determine one or more insights with respect to theknowledge graph 316. For example, in certain embodiments, the knowledgegraph component 306 employs regression analysis and/or one or moreclustering techniques to determine one or more insights associated withthe knowledge graph 316. In one or more embodiments, the knowledge graphcomponent 306 performs the machine learning process to determine one ormore categories and/or one or more patterns associated with theknowledge graph 316.

The action component 308 performs one or more actions based on the oneor more insights associated with the knowledge graph 316. For instance,in one or more embodiments, the action component 308 generates actiondata 322 associated with the one or more actions. In one or moreembodiments, the action component 308 additionally employs a scoringmodel based on different metrics from historical analysis with respectto the knowledge graph 316. Additionally or alternatively, in one ormore embodiments, the action component 308 additionally employs ascoring model based on different metrics from previous actions todetermine the one or more actions. For example, in one or moreembodiments, the scoring model employs weights for different metrics,different conditions, and/or different rules.

The operational limit component 338 additionally or alternativelyperforms one or more actions based on the one or more insightsassociated with the knowledge graph 316. For example, in one or moreembodiments, the operational limit component 338 adjusts one or moreoperational limits for the one or more assets based on the one or moreinsights associated with the knowledge graph 316. The operational limitsare, for example, one or more operational limits for one or moreprocesses related to the one or more assets. For example, theoperational limits include, for example, one or more alarm limits forone or more processes performed by the one or more assets, one or moreplanning limits for one or more operational plans associated with theone or more assets, one or more quality limits for one or more processesperformed by the one or more assets, one or more environment limits forone or more processes performed by the one or more assets, one or moresafety limits for one or more processes performed by the one or moreassets, and/or one or more other types of operational limits related tothe one or more assets. In another embodiment, an action from the one ormore actions includes transmitting, to a computing device, one or morenotifications associated with the one or more insights. In anotherembodiment, an action from the one or more actions includes updating oneor more portions of the knowledge graph 316 based on the one or moreinsights. In another embodiment, an action from the one or more actionsincludes determining one or more features associated with the one ormore insights. In another embodiment, an action from the one or moreactions includes performing one or more actions with respect to the oneor more assets based on the one or more insights. In another embodiment,an action from the one or more actions includes predicting, based on theone or more insights, a condition for the one or more assets. In anotherembodiment, an action from the one or more actions includes adjusting,based on the one or more insights, one or more limit settings and/oradjusting one or more processes associated with the one or more assets.In another embodiment, an action from the one or more actions includesdetermining, based on the one or more insights, likelihood of successfor a given scenario associated with the one or more assets. In anotherembodiment, an action from the one or more actions includes providing anoptimal process condition for the one or more assets. For example, inanother embodiment, an action from the one or more actions includesadjusting a set-point and/or a schedule for the one or more assets. Inanother embodiment, an action from the one or more actions includes oneor more corrective actions with respect to the one or more assets. Inanother embodiment, an action from the one or more actions includesproviding an optimal maintenance option for the one or more assets. Inanother embodiment, an action from the one or more actions includes anaction associated with the application services layer 225, theapplications layer 230, and/or the core services layer 235.

In one or more embodiments, the operational limit component 338generates one or more operational limit recommendations based on the oneor more insights. Furthermore, the operational limit component 338adjusts the one or more operational limits for the one or more assets inresponse to determining that a degree of deviation for the one or moreoperational limits satisfies a defined criterion. The operational limitcomponent 338 additionally or alternatively adjusts the one or moreoperational limits for the one or more assets in response to determiningthat adjustment of the one or more operational limits provides a certaindegree of optimization for one or more processes performed by the one ormore assets. In one or more embodiments, the operational limit component338 generates one or more integrity operating window recommendations forthe one or more assets based on the one or more insights. Furthermore,the operational limit component 338 adjusts the one or more operationallimits based on the one or more integrity operating windowrecommendations. An integrity operating window includes, for example, afirst operating limit (e.g., a lower limit) and a second operating limit(e.g., an upper limit) to define a range of limits for optimalperformance of an asset and/or a process related to the asset. In one ormore embodiments, the operational limit component 338 predicts one ormore operating conditions for the one or more assets based on the one ormore insights. Furthermore, the operational limit component 338 adjustsone or more operational limits for the one or more assets based on theone or more operating conditions for the one or more assets. In one ormore embodiments, the operational limit component 338 determines, basedon the one or more insights, a degree of correlation between two or moreportions of the aggregated operational technology data within theknowledge graph 316. Furthermore, the knowledge graph component 306updates the knowledge graph data structure based on the one or moreinsights in response to a determination that the degree of correlationcorresponds to a correlation threshold value.

The dashboard visualization component 348 additionally or alternativelyperforms one or more actions based on the one or more insightsassociated with the knowledge graph 316. In one or more embodiments, thedashboard visualization component 348 provides a dashboard visualizationto an electronic interface of a computing device. In one or moreembodiments, the dashboard visualization comprises visualization datafor the one or more insights associated with the knowledge graph 316. Inone or more embodiments, the knowledge graph component 306 determines apredicted operating state for the one or more assets based on the one ormore insights and the dashboard visualization component 348 configuresthe dashboard visualization based on the predicted operating state forthe one or more assets. For example, in one or more embodiments, theknowledge graph component 306 determines the predicted operating statebased on one or more relationships between attributes, features,different data types, different data nodes, and/or different portions ofthe aggregated operational technology data within the knowledge graphdata structure. The predicted operating state can correspond to apredicted set of operational settings and/or levels for the one or moreassets. In one or more embodiments, the knowledge graph component 306determines, based on the one or more insights, solution data for apredicted event associated with the one or more assets. In one or moreembodiments, the solution data includes one or more recommended changes,one or more recommended configurations, one or more recommendedoperational settings, and/or one or more other solutions for the one ormore assets to avoid the predicted event. The predicted event may be anundesirable event related to the one or more assets that reducesperformance for the one or more assets. Furthermore, the dashboardvisualization component 348 configures the dashboard visualization basedon the solution data. In one or more embodiments, the dashboardvisualization component 348 filters the visualization data for thedashboard visualization based on the user identifier, the one or moremetrics context identifiers, and/or the one or more time intervalidentifiers. In one or more embodiments, the knowledge graph component306 determines one or more operational limits for the one or more assetsbased on the one or more insights. Furthermore, the dashboardvisualization component 348 displays one or more graphical elementsassociated with the one or more operational limits via the dashboardvisualization.

In certain embodiments, the dashboard visualization component 348adjusts, via one or more actions performed with respect to the dashboardvisualization, one or more operational limits for the one or more assetsbased on the one or more insights associated with the knowledge graph316. The operational limits are, for example, one or more operationallimits for one or more processes related to the one or more assets. Forexample, the operational limits include, for example, one or more alarmlimits for one or more processes performed by the one or more assets,one or more planning limits for one or more operational plans associatedwith the one or more assets, one or more quality limits for one or moreprocesses performed by the one or more assets, one or more environmentlimits for one or more processes performed by the one or more assets,one or more safety limits for one or more processes performed by the oneor more assets, and/or one or more other types of operational limitsrelated to the one or more assets.

In one or more embodiments, the dashboard visualization includes one ormore metrics determined based on the knowledge graph 316. In one or moreembodiments, the dashboard visualization displays one or moreprioritized actions for the one or more assets determined based on theknowledge graph 316. In one or more embodiments, the action data 322and/or the dashboard visualization associated with the action data 322is configured based on one or more relationships associated with theknowledge graph 316. In one or more embodiments, the dashboardvisualization presents one or more dashboard visualization elementsassociated with process data, operations data, alarm data, planningdata, quality data, sensor data, control data, environment data, labormanagement data, warehouse execution data, inventory data, warehousemanagement, machine control data, and/or other data associated with theone or more assets. In one or more embodiments, the dashboardvisualization filters one or more events associated with the one or moreassets based on the one or more insights. In one or more embodiments,the dashboard visualization is configured for real-time collaborationbetween two or more computing devices based on the one or more insights.

FIG. 4 illustrates a system 300′ that provides an exemplary environmentaccording to one or more described features of one or more embodimentsof the disclosure. In an embodiment, the system 300′ corresponds to analternate embodiment of the system 300 shown in FIG. 3 . According to anembodiment, the system 300′ includes the enterprise data managementcomputer system 302, the edge devices 161 a-161 n, the one or moreoperational technology systems 318 and/or a computing device 402. In oneor more embodiments, the enterprise data management computer system 302is in communication with the edge devices 161 a-161 n and/or thecomputing device 402 via the network 110. The computing device 402 is amobile computing device, a smartphone, a tablet computer, a mobilecomputer, a desktop computer, a laptop computer, a workstation computer,a wearable device, a virtual reality device, an augmented realitydevice, or another type of computing device located remote from theenterprise data management computer system 302. In one or moreembodiments, the computing device 402 generates the request 320. Forexample, in one or more embodiments, the request 320 is generated via avisual display (e.g., a user interface) of the computing device 402. Inanother embodiment, the request 320 is generated via one or moremicrophones of the computing device 402 and/or one or more microphonescommunicatively coupled to the computing device 402. In anotherembodiment, the request 320 is generated via the edge devices 161 a-161n and/or a processing device (e.g., a controller) communicativelycoupled to the edge devices 161 a-161 n.

In one or more embodiments, the action component 308, the operationallimit component 338, and/or the dashboard visualization component 348communicates the action data 322 to the computing device 402. Forexample, in one or more embodiments, the action data 322 includes one ormore visual elements for a visual display (e.g., a user-interactiveelectronic interface) of the computing device 402 that renders a visualrepresentation of the one or more insights. In one or more otherembodiments, the action component 308 transmits the action data 322 tothe edge devices 161 a-161 n and/or a processing device (e.g., acontroller) communicatively coupled to the edge devices 161 a-161 n to,for example, alter one or more settings and/or one or more processes forthe one or more assets. In one or more embodiments, the action data 322includes one or more visual elements for a visual display (e.g., auser-interactive electronic interface) of the computing device 402 thatrenders a visual representation of prioritized actions for the one ormore assets. In certain embodiments, the visual display of the computingdevice 402 displays one or more graphical elements associated with theaction data 322. In another example, in one or more embodiments, theaction data 322 includes one or notifications associated with the one ormore metrics and/or the prioritized actions for the one or more assets.In one or more embodiments, the action data 322 allows a user associatedwith the computing device 402 to make decisions and/or perform one ormore actions with respect to the one or more assets. In one or moreembodiments, the action data 322 allows a user associated with thecomputing device 402 to control the one or more portions of the one ormore assets (e.g., one or more portions of the edge devices 161 a-161n). In one or more embodiments, the action data 322 allows a userassociated with the computing device 402 to generate one or more workorders for the one or more assets.

FIG. 5 illustrates a system 500 according to one or more embodiments ofthe disclosure. The system 500 includes the computing device 402. In oneor more embodiments, the computing device 402 employs mobile computing,augmented reality, cloud-based computing, IoT technology and/or one ormore other technologies to provide performance data, video, audio, text,graphs, charts, real-time data, graphical data, one or morecommunications, one or more messages, one or more notifications, and/orother media data associated with the one or more metrics. The computingdevice 402 includes mechanical components, electrical components,hardware components and/or software components to facilitate determiningprioritized actions and/or one or more metrics associated with the assetdata 314. In the embodiment shown in FIG. 5 , the computing device 402includes a visual display 504, one or more speakers 506, one or morecameras 508, one or more microphones 510, a global positioning system(GPS) device 512, a gyroscope 514, one or more wireless communicationdevices 516, and/or a power supply 518.

In an embodiment, the visual display 504 is a display that facilitatespresentation and/or interaction with one or more portions of thedashboard visualization data. In one or more embodiments, the computingdevice 402 displays an electronic interface (e.g., a graphical userinterface) associated with an asset performance management platform. Inone or more embodiments, the visual display 504 is a visual display thatrenders one or more interactive media elements via a set of pixels. Theone or more speakers 506 include one or more integrated speakers thatproject audio. The one or more cameras 508 include one or more camerasthat employ autofocus and/or image stabilization for photo captureand/or real-time video. In certain embodiments, the one or moremicrophones 510 include one or more digital microphones that employactive noise cancellation to capture audio data. The GPS device 512provides a geographic location for the computing device 402. Thegyroscope 514 provides an orientation for the computing device 402. Theone or more wireless communication devices 516 includes one or morehardware components to provide wireless communication via one or morewireless networking technologies and/or one or more short-wavelengthwireless technologies. The power supply 518 is, for example, a powersupply and/or a rechargeable battery that provides power to the visualdisplay 504, the one or more speakers 506, the one or more cameras 508,the one or more microphones 510, the GPS device 512, the gyroscope 514,and/or the one or more wireless communication devices 516. In certainembodiments, the action data 322 associated with the one or moreinsights related to the one or more assets is presented via the visualdisplay 504 and/or the one or more speakers 506.

FIG. 6 illustrates a system 600 according to one or more describedfeatures of one or more embodiments of the disclosure. The system 600includes the knowledge graph 316. In one or more embodiments, knowledgegraph data for the knowledge graph 316 is generated based on one or moredata sources 602. The one or more data sources 602 are one or more datasources for one or more assets 606. In one or more embodiments, the oneor more assets 606 correspond to the edge devices 161 a-161 n. In one ormore embodiments, the one or more data sources 602 correspond torespective data sources for the process data historian 321, the limitrepository 323, the operation monitoring 324, and/or the alarmmanagement 326. Additionally or alternatively, in one or moreembodiments, the one or more data sources 602 correspond to respectivedata sources for a manufacturing execution services associated with theone or more assets 606, asset performance monitoring associated with theone or more assets 606, a distributed control system associated with theone or more assets 606, a network system associated with the one or moreassets 606, a plant control network associated with the one or moreassets 606, a camera system associated with the one or more assets 606,site schematics associated with the one or more assets 606, and/oranother system associated with the one or more assets 606. In one ormore embodiments, data from the one or more data sources 602 are storedin one or more data files 604. In certain embodiments, respective datafrom respective data sources 602 is stored in respective data files 604.

In one or more embodiments, the knowledge graph component 306contextualizes operational technology data aggregated from the one ormore data sources 602 and/or the one or more data files 604. In one ormore embodiments, the knowledge graph component 306 contextualizes theoperational technology data based on configuration data for the one ormore assets 606 and/or a set of contextualization rules for the one ormore data sources 602. In one or more embodiments, the configurationdata for the one or more assets 606 includes configuration data relatedto process thresholds, process limits, process targets, processtolerances, alarm criteria, planning criteria, quality criteria,environment criteria, manufacturing execution services, operationsmonitoring, asset performance monitoring, distributed control systems,alarm management, plant historians, integrity operating windows, assetstags, virtual processes for an asset, network layers, processcharacterizations, process measurements, and/or process controlsassociated with the one or more assets 606. In certain embodiments, theconfiguration data for the one or more assets 606 includes configurationdata related to one or more processes (e.g., one or more controlprocesses, one or more autonomous control processes, one or more controlloops, one or more batch processes, one or more flow processes, etc.)related to the one or more assets 606, one or more controllersassociated with the one or more assets 606, one or more serversassociated with the one or more assets 606, input/output control for theone or more assets 606, and/or other control configurations associatedwith the one or more assets 606. In one or more embodiments, the set ofcontextualization rules for the one or more data sources 602 includesrules for one or more tags, one or more labels, one or more attributes,and/or one or more other classifications to contextualize theoperational technology data associated with the one or more data sources602. In one or more embodiments, the set of contextualization rules forthe one or more data sources includes rules for one or more weights todetermine one or more tags, one or more labels, one or more attributes,and/or one or more other classifications to contextualize theoperational technology data associated with the one or more data sources602. In one or more embodiments, the set of contextualization rules area set of contextualization rules for one or more data formats associatedwith the one or more data files 604. For example, in one or moreembodiments, the set of contextualization rules includes respectiveparsing rules and/or classification rules based on a format of the oneor more data files 604.

In an embodiment, the data sources 602 include a first data sourceassociated with alarm performance management, a second data sourceassociated with asset performance management, and a third data sourceassociated with control performance management. Furthermore, in one ormore embodiments, the knowledge graph component 306 weights first dataassociated with the first data source, second data associated with thesecond data source, and third data associated with the third data sourcein parallel to facilitate allocating the data within the knowledge graph316. In certain embodiments, the knowledge graph component 306contextualizes the first data associated with the first data source,second data associated with the second data source, and third dataassociated with the third data source based on a user identifierassociated with the request 320. Additionally or alternatively, incertain embodiments, the knowledge graph component 306 contextualizesthe first data associated with the first data source, second dataassociated with the second data source, and third data associated withthe third data source based on one or more events associated with theone or more assets 606. In one or more embodiments, the knowledge graph316 models ontological relationships between data from different levelsof an enterprise system (e.g., enterprise level, industrial site level,process/asset level, etc.).

FIG. 7 illustrates a system 700 according to one or more describedfeatures of one or more embodiments of the disclosure. The system 700includes a cognitive advisor 702 that employs the knowledge graph 316 todetermine one or more operational limit recommendations 704. Forexample, in various embodiments, the cognitive advisor 702 obtains oneor more insights with respect to the knowledge graph 316 to determinethe one or more operational limit recommendations 704. In variousembodiments, the cognitive advisor 702 corresponds to the operationallimit component 338 of the enterprise data management computer system302. The one or more operational limit recommendations 704 are, forexample, one or more operational limit recommendations for one orprocesses related to an asset. In certain embodiments, the one or moreoperational limit recommendations 704 include a recommended integrityoperating window for a process related to an asset. For example, incertain embodiments, the one or more operational limit recommendations704 include a first operating limit (e.g., a lower limit) and a secondoperating limit (e.g., an upper limit) to define a range of limits foroptimal performance of an asset and/or a process related to the asset.In various embodiments, the cognitive advisor 702 determines the one ormore operational limit recommendations 704 in response to receiving therequest 320.

In one or more embodiments, knowledge graph data for the knowledge graph316 is generated based on one or more data sources. The one or more datasources are one or more data sources for one or more assets. In one ormore embodiments, the one or more assets correspond to the edge devices161 a-161 n. In one or more embodiments, the one or more data sourcescorrespond to respective data sources for the process data historian321, the limit repository 323, the operation monitoring 324, and/or thealarm management 326. Additionally or alternatively, in one or moreembodiments, the one or more data sources correspond to respective datasources for a manufacturing execution services associated with the oneor more assets, asset performance monitoring associated with the one ormore assets, a distributed control system associated with the one ormore assets, a network system associated with the one or more assets, aplant control network associated with the one or more assets, a camerasystem associated with the one or more assets, site schematicsassociated with the one or more assets, and/or another system associatedwith the one or more assets. In one or more embodiments, data from theone or more data sources are stored in one or more data files. Incertain embodiments, respective data from respective data sources isstored in respective data files.

In one or more embodiments, the knowledge graph component 306contextualizes operational technology data aggregated from the one ormore data sources and/or the one or more data files. In one or moreembodiments, the knowledge graph component 306 contextualizes theoperational technology data based on configuration data for the one ormore assets and/or a set of contextualization rules for the one or moredata sources. In one or more embodiments, the configuration data for theone or more assets includes configuration data related to processthresholds, process limits, process targets, process tolerances, alarmcriteria, planning criteria, quality criteria, environment criteria,manufacturing execution services, operations monitoring, assetperformance monitoring, distributed control systems, alarm management,plant historians, integrity operating windows, assets tags, virtualprocesses for an asset, network layers, process characterizations,process measurements, and/or process controls associated with the one ormore assets. In certain embodiments, the configuration data for the oneor more assets includes configuration data related to one or moreprocesses (e.g., one or more control processes, one or more autonomouscontrol processes, one or more control loops, one or more batchprocesses, one or more flow processes, etc.) related to the one or moreassets, one or more controllers associated with the one or more assets,one or more servers associated with the one or more assets, input/outputcontrol for the one or more assets, and/or other control configurationsassociated with the one or more assets. In one or more embodiments, theset of contextualization rules for the one or more data sources includesrules for one or more tags, one or more labels, one or more attributes,and/or one or more other classifications to contextualize theoperational technology data associated with the one or more datasources. In one or more embodiments, the set of contextualization rulesfor the one or more data sources includes rules for one or more weightsto determine one or more tags, one or more labels, one or moreattributes, and/or one or more other classifications to contextualizethe operational technology data associated with the one or more datasources. In one or more embodiments, the set of contextualization rulesare a set of contextualization rules for one or more data formatsassociated with the one or more data files. For example, in one or moreembodiments, the set of contextualization rules includes respectiveparsing rules and/or classification rules based on a format of the oneor more data files.

FIG. 8 illustrates a system 800 according to one or more describedfeatures of one or more embodiments of the disclosure. The system 800includes the enterprise data management computer system 302 that employsthe knowledge graph 316 to determine a dashboard visualization 804. Forexample, in various embodiments, the enterprise data management computersystem 302 obtains one or more insights with respect to the knowledgegraph 316 to determine the dashboard visualization 804. In one or moreembodiments, the dashboard visualization 804 is rendered via the visualdisplay 504 of the computing device 402. In one or more embodiments, thedashboard visualization 804 the dashboard visualization includesvisualization data for one or more insights associated with theknowledge graph 316. In various embodiments, the dashboard visualization804 presents one or more operational limit recommendations for one orprocesses related to an asset. In certain embodiments, the one or moreoperational limit recommendations include a recommended integrityoperating window for a process related to an asset. For example, incertain embodiments, the one or more operational limit recommendationsinclude a first operating limit (e.g., a lower limit) and a secondoperating limit (e.g., an upper limit) to define a range of limits foroptimal performance of an asset and/or a process related to the asset.In various embodiments, the enterprise data management computer system302 generates the dashboard visualization 804 in response to receivingthe request 320.

In one or more embodiments, knowledge graph data for the knowledge graph316 is generated based on one or more data sources. The one or more datasources are one or more data sources for one or more assets. In one ormore embodiments, the one or more assets correspond to the edge devices161 a-161 n. In one or more embodiments, the one or more data sourcescorrespond to respective data sources for the process data historian321, the limit repository 323, the operation monitoring 324, and/or thealarm management 326. Additionally or alternatively, in one or moreembodiments, the one or more data sources correspond to respective datasources for a manufacturing execution services associated with the oneor more assets, asset performance monitoring associated with the one ormore assets, a distributed control system associated with the one ormore assets, a network system associated with the one or more assets, aplant control network associated with the one or more assets, a camerasystem associated with the one or more assets, site schematicsassociated with the one or more assets, and/or another system associatedwith the one or more assets. In one or more embodiments, data from theone or more data sources are stored in one or more data files. Incertain embodiments, respective data from respective data sources isstored in respective data files.

In one or more embodiments, the knowledge graph component 306contextualizes operational technology data aggregated from the one ormore data sources and/or the one or more data files. In one or moreembodiments, the knowledge graph component 306 contextualizes theoperational technology data based on configuration data for the one ormore assets and/or a set of contextualization rules for the one or moredata sources. In one or more embodiments, the configuration data for theone or more assets includes configuration data related to processthresholds, process limits, process targets, process tolerances, alarmcriteria, planning criteria, quality criteria, environment criteria,manufacturing execution services, operations monitoring, assetperformance monitoring, distributed control systems, alarm management,plant historians, integrity operating windows, assets tags, virtualprocesses for an asset, network layers, process characterizations,process measurements, and/or process controls associated with the one ormore assets. In certain embodiments, the configuration data for the oneor more assets includes configuration data related to one or moreprocesses (e.g., one or more control processes, one or more autonomouscontrol processes, one or more control loops, one or more batchprocesses, one or more flow processes, etc.) related to the one or moreassets, one or more controllers associated with the one or more assets,one or more servers associated with the one or more assets, input/outputcontrol for the one or more assets, and/or other control configurationsassociated with the one or more assets. In one or more embodiments, theset of contextualization rules for the one or more data sources includesrules for one or more tags, one or more labels, one or more attributes,and/or one or more other classifications to contextualize theoperational technology data associated with the one or more datasources. In one or more embodiments, the set of contextualization rulesfor the one or more data sources includes rules for one or more weightsto determine one or more tags, one or more labels, one or moreattributes, and/or one or more other classifications to contextualizethe operational technology data associated with the one or more datasources. In one or more embodiments, the set of contextualization rulesare a set of contextualization rules for one or more data formatsassociated with the one or more data files. For example, in one or moreembodiments, the set of contextualization rules includes respectiveparsing rules and/or classification rules based on a format of the oneor more data files.

FIG. 9 illustrates a non-limiting embodiment of the knowledge graph 316according to one or more described features of one or more embodimentsof the disclosure. The knowledge graph 316 includes a set of nodesand/or a set of connectors that graphically represents data andrespective ontological relationships between data associated with adistributed control system (DCS) process. The DCS process is, forexample, a DCS process for one or more assets of an industrial plant. Inone or more embodiments, the set of nodes correspond to knowledge graphdata elements related to the DCS process. In a non-limiting embodiment,the knowledge graph 316 includes a node 902 that corresponds to the DCSprocess, a node 904 that corresponds to one or more units associatedwith the DCS process, a node 906 that corresponds to one or more alarmconfigurations for the DCS process, a node 908 that corresponds to acloud network for the DCS process, a node 910 that corresponds to one ormore schematics (e.g., one or more electrical schematics, one or morelayout schematics, one or more mechanical schematics, etc.) for the DCSprocess, a node 912 that corresponds to a group display (e.g., computergraphics data) for the DCS process, a node 914 that corresponds to level3 process historian data (e.g., level 3 process historian tag(s))associated with production control for the DCS process, a node 916 thatcorresponds to one or more virtual calculations (e.g., one or moresimulations and/or one or more virtual machine processes) for theproduction control associated with the DCS process, and/or a node 918that corresponds to a sub-process (e.g., one or more DCS points) for theDCS process. Additionally, in a non-limiting embodiment, the knowledgegraph 316 includes a node 920 that corresponds to level 4 processhistorian data (e.g., level 4 process historian tag(s)) associated withproduction scheduling for the DCS process, a node 922 that correspondsto one or more virtual calculations (e.g., one or more simulationsand/or one or more virtual machine processes) for the productionscheduling associated with the DCS process, a node 924 that correspondsto one or more targets for the DCS process, a node 926 that correspondsto one or more assets related to the DCS process, and/or a node 928 thatcorresponds to one or more limits for the DCS process. In one or moreembodiments, connections between respective nodes of the knowledge graph316 illustrate respective ontological relationships between data for therespective nodes.

FIG. 10 illustrates a system 1000 according to one or more describedfeatures of one or more embodiments of the disclosure. The system 1000includes the enterprise data management computer system 302. In one ormore embodiments, the enterprise data management computer system 302receives operational technology data 1002, contextualizes theoperational technology data 1002 to generate knowledge graph data forthe knowledge graph 316, and determines one or more insights 1004 basedon ontological relationships with respect to knowledge graph dataallocated within the knowledge graph 316. In various embodiments, theoperational technology data 1002 can be consolidated data for theenterprise data management computer system 302. In an embodiment, theoperational technology data 1002 includes process data 1006 associatedwith the process data historian 321, limit store data 1008 associatedwith the limit repository 323, operations deviation history data 1010associated with the operation monitoring 324, and/or alarm history data1012 associated with the alarm management 326. In one or moreembodiments, the limit store data 1008 includes one or more alarm limits1018 for the one or more assets, one or more planning limits 1028 forthe one or more assets, one or more quality limits 1038 for the one ormore assets, and/or one or more environment limits 1048 for the one ormore assets. In one or more embodiments, the enterprise data managementcomputer system 302 can be configured as an analytics engine to processthe one or more alarm limits 1018, the one or more planning limits 1028,the one or more quality limits 1038, and/or the one or more environmentlimits 1048. In one or more embodiments, the one or more insights 1004includes one or more recommendations for modifying one or more limitsfrom the one or more alarm limits 1018, the one or more planning limits1028, the one or more quality limits 1038, and/or the one or moreenvironment limits 1048. In certain embodiments, the one or moreinsights 1004 includes one or more recommended corrections, one or morerecommended thresholds, and/or one or more recommended tolerances forthe one or more alarm limits 1018, the one or more planning limits 1028,the one or more quality limits 1038, and/or the one or more environmentlimits 1048.

FIG. 11 illustrates a process visualization 1100 according to one ormore described features of one or more embodiments of the disclosure.The process visualization 1100 is, for example, a visualization foroperational technology data 1102 related to a process for an asset. Inan embodiment, the process for the asset employs an integrity operatingwindow for the process that includes a first operating limit 1104 (e.g.,a lower limit) and a second operating limit 1106 (e.g., an upper limit)to define a range of limits for performance of the asset and/or theprocess for the asset. In one or more embodiments, the knowledge graphcomponent 306 correlates aspects of aggregated operational technologydata (e.g., aggregated operational technology data associated with theasset, the process for the asset, and/or the operational technology data1102) within the knowledge graph 316 to provide one or more insights forthe integrity operating window. Additionally, in one or moreembodiments, the operational limit component 338 adjusts the integrityoperating window based on the one or more insights associated with theknowledge graph 316. For example, in an embodiment, the operationallimit component 338 determines a new operating limit 1108 (e.g., a newupper limit) based on an insight 1110 (e.g., an upper limit prediction)such that a new range of limits that includes the first operating limit1104 and the new operating limit 1108 provide optimal performance forthe asset and/or the process related to the asset. In certainembodiments, the new operating limit 1108 provides an early warning fora potential undesirable event associated with the asset and/or theprocess related to the asset. In one or more embodiments, an advisoryalert associated with the new operating limit 1108 is generated.

FIG. 12 illustrates a method 1200 for generating a knowledge graph forone or more assets, in accordance with one or more embodiments describedherein. The method 1200 is associated with the enterprise datamanagement computer system 302, for example. For instance, in one ormore embodiments, the method 1200 is executed at a device (e.g., theenterprise data management computer system 302) with one or moreprocessors and a memory. In one or more embodiments, the method 1200begins at block 1202 that receives (e.g., by the data aggregationcomponent 304 and/or the knowledge graph component 306) a request togenerate knowledge graph data related to one or more assets, the requestcomprising an asset descriptor describing the one or more assets. Therequest provides one or more technical improvements such as, but notlimited to, facilitating interaction with a computing device and/orextended functionality for a computing device.

At block 1204, it is determined whether the request is processed. If no,block 1204 is repeated to determine whether the request is processed. Ifyes, the method 1200 proceeds to block 1206. In response to the request,the method 1200 includes a block 1206 that obtains (e.g., by the dataaggregation component 304) aggregated operational technology data fromone or more data sources associated with the one or more assets based onthe asset descriptor. The obtaining the aggregated operationaltechnology data provides one or more technical improvements such as, butnot limited to, extended functionality for a computing device.

The method 1200 also includes a block 1208 that, in response to therequest, contextualizes, based on configuration data for the one orassets and a set of contextualization rules for the one or more datasources (e.g., by the knowledge graph component 306), the aggregatedoperational technology data to generate the knowledge graph data. Thecontextualizing provides one or more technical improvements such as, butnot limited to, improving accuracy of a knowledge graph data structureand/or performance of one or more assets. In one or more embodiments,the contextualizing includes contextualizing the aggregated operationaltechnology data based on a set of contextualization rules for one ormore data formats associated with the one or more data sources. In oneor more embodiments, the contextualizing includes parsing the aggregatedoperational technology data based on metadata associated with the useridentifier. In one or more embodiments, the contextualizing includesparsing the aggregated operational technology data based on metadataassociated with the one or more assets. In one or more embodiments, thecontextualizing includes parsing the aggregated operational technologydata based on one or more parameters associated with a contextualizationtechnique associated with the set of contextualization rules. In one ormore embodiments, the contextualizing includes parsing alarm limit data,planning limit data, quality limit data, environment limit data, orother limit data for the one or more assets based on the configurationdata for the one or assets and the set of contextualization rules forthe one or more data sources. In one or more embodiments, thecontextualizing includes parsing operation deviation data associatedwith monitoring of the one or more assets based on the configurationdata for the one or assets and the set of contextualization rules forthe one or more data sources. In one or more embodiments, thecontextualizing includes parsing alarm history data associated with theone or more assets based on the configuration data for the one or assetsand the set of contextualization rules for the one or more data sources.

The method 1200 also includes a block 1210 that, in response to therequest, allocates (e.g., by the knowledge graph component 306) theknowledge graph data within a knowledge graph data structure configuredfor the one or more assets. The allocating provides one or moretechnical improvements such as, but not limited to, improving accuracyof a knowledge graph data structure and/or performance of one or moreassets. In one or more embodiments, the allocating the knowledge graphdata within the knowledge graph data structure includes organizing theknowledge graph data based on an ontological data structure thatcaptures relationships among respective knowledge graph data portionswithin the knowledge graph data structure.

In one or more embodiments, the method 1200 further includes performingone or more actions with respect to the one or more assets based on theknowledge graph data structure. For example, the one or more action caninclude predicting one or more conditions for the one or more assetsbased on the knowledge graph data structure, adjusting one or more limitsettings for the one or more assets based on the knowledge graph datastructure, adjusting one or more process thresholds for the one or moreassets based on the knowledge graph data structure, generating auser-interactive electronic interface that renders a visualrepresentation of data related to the one or more assets based on theknowledge graph data structure, generating one or more notificationsrelated to the one or more assets based on the knowledge graph datastructure, and/or one or more other actions related to the one or moreassets.

In one or more embodiments, the request further comprising a useridentifier describing a user role for a user associated with therequest. Additionally, in one or more embodiments and in response to therequest, the method 1200 further includes contextualizing the aggregatedoperational technology data based on the user identifier. In one or moreembodiments and in response to the request, the method 1200 furtherincludes selecting the knowledge graph data structure for the knowledgegraph data based on the user identifier. In one or more embodiments andin response to the request, the method 1200 further includes allocatingthe knowledge graph data within the knowledge graph data structure basedon the user identifier.

In one or more embodiments, the method 1200 further includes obtainingone or more insights with respect to the knowledge graph data allocatedwithin the knowledge graph data structure. In one or more embodiments,the method 1200 further includes generating a user-interactiveelectronic interface that renders a visual representation of the one ormore insights. In one or more embodiments, the method 1200 furtherincludes generating one or more notifications associated with the one ormore insights. In one or more embodiments, the method 1200 furtherincludes performing one or more actions with respect to the one or moreassets based on the one or more insights. In one or more embodiments,the method 1200 further includes predicting one or more conditions forthe one or more assets based on the one or more insights. In one or moreembodiments, the method 1200 further includes adjusting one or morelimit settings for the one or more assets based on the one or moreinsights. In one or more embodiments, the method 1200 further includesadjusting one or more process thresholds for the one or more assetsbased on the one or more insights.

FIG. 13 illustrates a method 1300 for integrity operating windowoptimization for one or more assets, in accordance with one or moreembodiments described herein. The method 1300 is associated with theenterprise data management computer system 302, for example. Forinstance, in one or more embodiments, the method 1300 is executed at adevice (e.g., the enterprise data management computer system 302) withone or more processors and a memory. In one or more embodiments, themethod 1300 begins at block 1302 that receives (e.g., by the dataaggregation component 304 and/or the knowledge graph component 306) arequest to obtain one or more insights related to one or more assets,the request comprising an asset descriptor describing the one or moreassets. The request provides one or more technical improvements such as,but not limited to, facilitating interaction with a computing deviceand/or extended functionality for a computing device.

At block 1304, it is determined whether the request is processed. If no,block 1304 is repeated to determine whether the request is processed. Ifyes, the method 1300 proceeds to block 1306. In response to the request,the method 1300 includes a block 1306 that correlates (e.g., by theknowledge graph component 306) aspects of aggregated operationaltechnology data within a knowledge graph data structure, based on theasset descriptor, to provide the one or more insights. In one or moreembodiments, the knowledge graph data structure is configured as anontological data structure that captures relationships among respectiveaggregated operational technology data within the knowledge graph datastructure. In one or more embodiments, attributes, features, differentdata types, different data nodes, and/or different portions of theaggregated operational technology data within the knowledge graph datastructure are correlated to provide the one or more insights associatedwith the one or more assets. The correlating provides one or moretechnical improvements such as, but not limited to, extendedfunctionality for a computing device and/or improving performance of oneor more assets.

The method 1300 also includes a block 1308 that, in response to therequest, adjusts (e.g., by the operational limit component 338) one ormore operational limits for the one or more assets based on the one ormore insights associated with the knowledge graph data structure. Theadjusting provides one or more technical improvements such as, but notlimited to, improving operational limits for one or more assets and/orimproving performance of one or more assets. In one or more embodiments,the adjusting the one or more operational limits includes altering oneor more alarm limits for one or more processes performed by the one ormore assets. In one or more embodiments, the adjusting the one or moreoperational limits includes altering one or more planning limits for oneor more operational plans associated with the one or more assets. In oneor more embodiments, the adjusting the one or more operational limitsincludes altering one or more quality limits for one or more processesperformed by the one or more assets. In one or more embodiments, theadjusting the one or more operational limits includes altering one ormore environment limits for one or more processes performed by the oneor more assets. In one or more embodiments, the adjusting the one ormore operational limits includes altering one or more safety limits forone or more processes performed by the one or more assets.

In one or more embodiments, the method 1300 further includes generatingone or more operational limit recommendations based on the one or moreinsights. In one or more embodiments, the method 1300 further includesadjusting the one or more operational limits for the one or more assetsin response to determining that a degree of deviation for the one ormore operational limits satisfies a defined criterion.

In one or more embodiments, the method 1300 further includes generatingone or more operational limit recommendations based on the one or moreinsights. In one or more embodiments, the method 1300 further includesadjusting the one or more operational limits for the one or more assetsin response to determining that adjustment of the one or moreoperational limits provides a certain degree of optimization for one ormore processes performed by the one or more assets.

In one or more embodiments, the method 1300 further includes generatingone or more integrity operating window recommendations for the one ormore assets based on the one or more insights. In one or moreembodiments, the method 1300 further includes adjusting the one or moreoperational limits based on the one or more integrity operating windowrecommendations.

In one or more embodiments, the method 1300 further includes predictingone or more operating conditions for the one or more assets based on theone or more insights. In one or more embodiments, the method 1300further includes adjusting one or more operational limits for the one ormore assets based on the one or more operating conditions for the one ormore assets.

In one or more embodiments, the method 1300 further includesdetermining, based on the one or more insights, a degree of correlationbetween two or more portions of the aggregated operational technologydata within the knowledge graph data structure. In one or moreembodiments, the method 1300 further includes updating the knowledgegraph data structure based on the one or more insights in response to adetermination that the degree of correlation corresponds to acorrelation threshold value.

In one or more embodiments, the request further comprising a useridentifier describing a user role for a user associated with therequest, and, in response to the request, the method 1300 furtherincludes correlating the aspects of aggregated operational technologydata based on the user identifier to provide the one or more insights.

In one or more embodiments, the method 1300 further includes generatinga user-interactive electronic interface that renders a visualrepresentation of the one or more insights. In one or more embodiments,the method 1300 further includes generating one or more notificationsassociated with the one or more insights. In one or more embodiments,the method 1300 further includes performing one or more actions withrespect to the one or more assets based on the one or more insights. Inone or more embodiments, the method 1300 further includes predicting oneor more conditions for the one or more assets based on the one or moreinsights. In one or more embodiments, the method 1300 further includesadjusting one or more limit settings for the one or more assets based onthe one or more insights. In one or more embodiments, the method 1300further includes adjusting one or more process thresholds for the one ormore assets based on the one or more insights.

FIG. 14 illustrates a method 1400 for providing an enterprise datamanagement dashboard for one or more assets, in accordance with one ormore embodiments described herein. The method 1400 is associated withthe enterprise data management computer system 302, for example. Forinstance, in one or more embodiments, the method 1400 is executed at adevice (e.g. the enterprise data management computer system 302) withone or more processors and a memory. In one or more embodiments, themethod 1400 begins at block 1402 that receives (e.g., by the knowledgegraph component 306 and/or the dashboard visualization component 348) arequest to generate a dashboard visualization related to one or moreassets, the request comprising an asset descriptor describing the one ormore assets. The request provides one or more technical improvementssuch as, but not limited to, facilitating interaction with a computingdevice and/or extended functionality for a computing device.

At block 1404, it is determined whether the request is processed. If no,block 1404 is repeated to determine whether the request is processed. Ifyes, the method 1400 proceeds to block 1406. In response to the request,the method 1400 includes a block 1406 that correlates (e.g., by theknowledge graph component 306) aspects of aggregated operationaltechnology data within a knowledge graph data structure, based on theasset descriptor, to provide one or more insights associated with theone or more assets. In one or more embodiments, attributes, features,different data types, different data nodes, and/or different portions ofthe aggregated operational technology data within the knowledge graphdata structure are correlated to provide the one or more insightsassociated with the one or more assets. The correlating provides one ormore technical improvements such as, but not limited to, extendedfunctionality for a computing device and/or improving performance of oneor more assets.

The method 1400 also includes a block 1408 that, in response to therequest, provides (e.g., by the dashboard visualization component 348)the dashboard visualization to an electronic interface of a computingdevice, the dashboard visualization comprising visualization data forthe one or more insights associated with the knowledge graph datastructure. The providing the dashboard visualization provides one ormore technical improvements such as, but not limited to, improving whatand/or how to present information via a computing device and/orimproving performance of one or more assets.

In one or more embodiments, the method 1400 further includes adjustingone or more operational settings (e.g., one or more operational limits)for the one or more assets based on the dashboard visualization.

In one or more embodiments, the method 1400 further includes determininga predicted operating state for the one or more assets based on the oneor more insights. Additionally, in one or more embodiments, the method1400 further includes configuring the dashboard visualization based onthe predicted operating state for the one or more assets. In one or moreembodiments, the method 1400 further includes determining the predictedoperating state for the one or more assets based on one or morerelationships between the attributes of the aggregated operationaltechnology data.

In one or more embodiments, the method 1400 further includesdetermining, based on the one or more insights, solution data for apredicted event associated with the one or more assets. Additionally, inone or more embodiments, the method 1400 further includes configuringthe dashboard visualization based on the solution data. In one or moreembodiments, the solution data includes one or more recommended changesfor the one or more assets to avoid the predicted event.

In one or more embodiments, the request further comprises a useridentifier describing a user role for a user associated with therequest. Additionally, in one or more embodiments, the method 1400further includes configuring the dashboard visualization based on theuser identifier. Additionally or alternatively, in one or moreembodiments, the method 1400 further includes filtering thevisualization data for the dashboard visualization based on the useridentifier.

In one or more embodiments, the request further comprises a metricscontext identifier describing context for the one or more insights.Additionally, in one or more embodiments, the method 1400 furtherincludes configuring the dashboard visualization based on the metricscontext identifier.

In one or more embodiments, the method 1400 further includes determiningone or more operational limits for the one or more assets based on theone or more insights. Additionally, in one or more embodiments, themethod 1400 further includes displaying one or more graphical elementsassociated with the one or more operational limits via the dashboardvisualization.

In one or more embodiments, the method 1400 further includes determiningone or more planning limits for one or more operational plans associatedwith the one or more assets based on the one or more insights.Additionally, in one or more embodiments, the method 1400 furtherincludes displaying one or more graphical elements associated with theone or more planning limits via the dashboard visualization.

In one or more embodiments, the method 1400 further includes determiningone or more quality limits for one or more processes performed by theone or more assets based on the one or more insights. Additionally, inone or more embodiments, the method 1400 further includes displaying oneor more graphical elements associated with the one or more qualitylimits via the dashboard visualization.

In one or more embodiments, the method 1400 further includes determiningone or more environment limits for one or more processes performed bythe one or more assets based on the one or more insights. Additionally,in one or more embodiments, the method 1400 further includes displayingone or more graphical elements associated with the one or moreenvironment limits via the dashboard visualization.

In one or more embodiments, the method 1400 further includes determiningone or more safety limits for one or more processes performed by the oneor more assets based on the one or more insights. Additionally, in oneor more embodiments, the method 1400 further includes displaying one ormore graphical elements associated with the one or more safety limitsvia the dashboard visualization.

In one or more embodiments, the method 1400 further includes generatingone or more operational limit recommendations based on the one or moreinsights. Additionally, in one or more embodiments, the method 1400further includes displaying one or more graphical elements associatedwith the one or more operational limit recommendations via the dashboardvisualization.

In one or more embodiments, the method 1400 further includes generatingone or more integrity operating window recommendations for the one ormore assets based on the one or more insights. Additionally, in one ormore embodiments, the method 1400 further includes displaying one ormore graphical elements associated with the one or more integrityoperating window recommendations via the dashboard visualization.

In one or more embodiments, the method 1400 further includes correlatingthe attributes of the aggregated operational technology data based onconnections between nodes of the knowledge graph data structure.

In one or more embodiments, the method 1400 further includes generatingone or more notifications associated with the one or more insights.Additionally, in one or more embodiments, the method 1400 furtherincludes displaying the one or more notifications via the dashboardvisualization.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the artthe order of steps in the foregoing embodiments can be performed in anyorder. Words such as “thereafter,” “then,” “next,” etc. are not intendedto limit the order of the steps; these words are simply used to guidethe reader through the description of the methods. Further, anyreference to claim elements in the singular, for example, using thearticles “a,” “an” or “the” is not to be construed as limiting theelement to the singular.

FIG. 15 depicts an example system 1500 that may execute techniquespresented herein. FIG. 15 is a simplified functional block diagram of acomputer that may be configured to execute techniques described herein,according to exemplary embodiments of the present disclosure.Specifically, the computer (or “platform” as it may not be a singlephysical computer infrastructure) may include a data communicationinterface 1560 for packet data communication. The platform also mayinclude a central processing unit (“CPU”) 1520, in the form of one ormore processors, for executing program instructions. The platform mayinclude an internal communication bus 1510, and the platform also mayinclude a program storage and/or a data storage for various data filesto be processed and/or communicated by the platform such as ROM 1530 andRAM 1540, although the system 1500 may receive programming and data vianetwork communications. The system 1500 also may include input andoutput ports 1550 to connect with input and output devices such askeyboards, mice, touchscreens, monitors, displays, etc. Of course, thevarious system functions may be implemented in a distributed fashion ona number of similar platforms, to distribute the processing load.Alternatively, the systems may be implemented by appropriate programmingof one computer hardware platform.

The general discussion of this disclosure provides a brief, generaldescription of a suitable computing environment in which the presentdisclosure may be implemented. In one embodiment, any of the disclosedsystems, methods, and/or graphical user interfaces may be executed by orimplemented by a computing system consistent with or similar to thatdepicted and/or explained in this disclosure. Although not required,aspects of the present disclosure are described in the context ofcomputer-executable instructions, such as routines executed by a dataprocessing device, e.g., a server computer, wireless device, and/orpersonal computer. Those skilled in the relevant art will appreciatethat aspects of the present disclosure can be practiced with othercommunications, data processing, or computer system configurations,including: Internet appliances, hand-held devices (including personaldigital assistants (“PDAs”)), wearable computers, all manner of cellularor mobile phones (including Voice over IP (“VoIP”) phones), dumbterminals, media players, gaming devices, virtual reality devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, set-top boxes, network PCs, mini-computers, mainframecomputers, and the like. Indeed, the terms “computer,” “server,” and thelike, are generally used interchangeably herein, and refer to any of theabove devices and systems, as well as any data processor.

Aspects of the present disclosure may be embodied in a special purposecomputer and/or data processor that is specifically programmed,configured, and/or constructed to perform one or more of thecomputer-executable instructions explained in detail herein. Whileaspects of the present disclosure, such as certain functions, aredescribed as being performed exclusively on a single device, the presentdisclosure also may be practiced in distributed environments wherefunctions or modules are shared among disparate processing devices,which are linked through a communications network, such as a Local AreaNetwork (“LAN”), Wide Area Network (“WAN”), and/or the Internet.Similarly, techniques presented herein as involving multiple devices maybe implemented in a single device. In a distributed computingenvironment, program modules may be located in both local and/or remotememory storage devices.

Aspects of the present disclosure may be stored and/or distributed onnon-transitory computer-readable media, including magnetically oroptically readable computer discs, hard-wired or preprogrammed chips(e.g., EEPROM semiconductor chips), nanotechnology memory, biologicalmemory, or other data storage media. Alternatively, computer implementedinstructions, data structures, screen displays, and other data underaspects of the present disclosure may be distributed over the Internetand/or over other networks (including wireless networks), on apropagated signal on a propagation medium (e.g., an electromagneticwave(s), a sound wave, etc.) over a period of time, and/or they may beprovided on any analog or digital network (packet switched, circuitswitched, or other scheme).

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine-readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links, or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

It is to be appreciated that ‘one or more’ includes a function beingperformed by one element, a function being performed by more than oneelement, e.g., in a distributed fashion, several functions beingperformed by one element, several functions being performed by severalelements, or any combination of the above.

Moreover, it will also be understood that, although the terms first,second, etc. are, in some instances, used herein to describe variouselements, these elements should not be limited by these terms. Theseterms are only used to distinguish one element from another. Forexample, a first contact could be termed a second contact, and,similarly, a second contact could be termed a first contact, withoutdeparting from the scope of the various described embodiments. The firstcontact and the second contact are both contacts, but they are not thesame contact.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when”or “upon” or “in response to determining” or “in response to detecting,”depending on the context. Similarly, the phrase “if it is determined” or“if [a stated condition or event] is detected” is, optionally, construedto mean “upon determining” or “in response to determining” or “upondetecting [the stated condition or event]” or “in response to detecting[the stated condition or event],” depending on the context.

The systems, apparatuses, devices, and methods disclosed herein aredescribed in detail by way of examples and with reference to thefigures. The examples discussed herein are examples only and areprovided to assist in the explanation of the apparatuses, devices,systems, and methods described herein. None of the features orcomponents shown in the drawings or discussed below should be taken asmandatory for any specific implementation of any of these theapparatuses, devices, systems or methods unless specifically designatedas mandatory. For ease of reading and clarity, certain components,modules, or methods may be described solely in connection with aspecific figure. In this disclosure, any identification of specifictechniques, arrangements, etc. are either related to a specific examplepresented or are merely a general description of such a technique,arrangement, etc. Identifications of specific details or examples arenot intended to be, and should not be, construed as mandatory orlimiting unless specifically designated as such. Any failure tospecifically describe a combination or sub-combination of componentsshould not be understood as an indication that any combination orsub-combination is not possible. It will be appreciated thatmodifications to disclosed and described examples, arrangements,configurations, components, elements, apparatuses, devices, systems,methods, etc. can be made and may be desired for a specific application.Also, for any methods described, regardless of whether the method isdescribed in conjunction with a flow diagram, it should be understoodthat unless otherwise specified or required by context, any explicit orimplicit ordering of steps performed in the execution of a method doesnot imply that those steps must be performed in the order presented butinstead may be performed in a different order or in parallel.

Throughout this disclosure, references to components or modulesgenerally refer to items that logically can be grouped together toperform a function or group of related functions. Like referencenumerals are generally intended to refer to the same or similarcomponents. Components and modules can be implemented in software,hardware, or a combination of software and hardware. The term “software”is used expansively to include not only executable code, for examplemachine-executable or machine-interpretable instructions, but also datastructures, data stores and computing instructions stored in anysuitable electronic format, including firmware, and embedded software.The terms “information” and “data” are used expansively and includes awide variety of electronic information, including executable code;content such as text, video data, and audio data, among others; andvarious codes or flags. The terms “information,” “data,” and “content”are sometimes used interchangeably when permitted by context.

The hardware used to implement the various illustrative logics, logicalblocks, modules, and circuits described in connection with the aspectsdisclosed herein can include a general purpose processor, a digitalsignal processor (DSP), a special-purpose processor such as anapplication specific integrated circuit (ASIC) or a field programmablegate array (FPGA), a programmable logic device, discrete gate ortransistor logic, discrete hardware components, or any combinationthereof designed to perform the functions described herein. Ageneral-purpose processor can be a microprocessor, but, in thealternative, the processor can be any processor, controller,microcontroller, or state machine. A processor can also be implementedas a combination of computing devices, e.g., a combination of a DSP anda microprocessor, a plurality of microprocessors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration. Alternatively, or in addition, some steps or methods canbe performed by circuitry that is specific to a given function.

In one or more example embodiments, the functions described herein canbe implemented by special-purpose hardware or a combination of hardwareprogrammed by firmware or other software. In implementations relying onfirmware or other software, the functions can be performed as a resultof execution of one or more instructions stored on one or morenon-transitory computer-readable media and/or one or more non-transitoryprocessor-readable media. These instructions can be embodied by one ormore processor-executable software modules that reside on the one ormore non-transitory computer-readable or processor-readable storagemedia. Non-transitory computer-readable or processor-readable storagemedia can in this regard comprise any storage media that can be accessedby a computer or a processor. By way of example but not limitation, suchnon-transitory computer-readable or processor-readable media can includerandom access memory (RAM), read-only memory (ROM), electricallyerasable programmable read-only memory (EEPROM), FLASH memory, diskstorage, magnetic storage devices, or the like. Disk storage, as usedherein, includes compact disc (CD), laser disc, optical disc, digitalversatile disc (DVD), floppy disk, and Blu-ray Disc™, or other storagedevices that store data magnetically or optically with lasers.Combinations of the above types of media are also included within thescope of the terms non-transitory computer-readable andprocessor-readable media. Additionally, any combination of instructionsstored on the one or more non-transitory processor-readable orcomputer-readable media can be referred to herein as a computer programproduct.

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of teachings presented in theforegoing descriptions and the associated drawings. Although the figuresonly show certain components of the apparatus and systems describedherein, it is understood that various other components can be used inconjunction with the supply management system. Therefore, it is to beunderstood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Moreover, the steps in the method described above can not necessarilyoccur in the order depicted in the accompanying diagrams, and in somecases one or more of the steps depicted can occur substantiallysimultaneously, or additional steps can be involved. Although specificterms are employed herein, they are used in a generic and descriptivesense only and not for purposes of limitation.

It is intended that the specification and examples be considered asexemplary only, with a true scope and spirit of the disclosure beingindicated by the following claims.

What is claimed is:
 1. A system, comprising: one or more processors; amemory; and one or more programs stored in the memory, the one or moreprograms comprising instructions configured to: receive a request togenerate a dashboard visualization related to one or more assets, therequest comprising: an asset descriptor describing the one or moreassets; in response to the request: correlate, based on the assetdescriptor, attributes of aggregated operational technology data withina knowledge graph data structure to provide one or more insightsassociated with the one or more assets; and provide the dashboardvisualization to an electronic interface of a computing device, thedashboard visualization comprising visualization data for the one ormore insights associated with the knowledge graph data structure; andadjust one or more operational settings for the one or more assets basedon the dashboard visualization.
 2. The system of claim 1, the one ormore programs further comprising instructions configured to: determine apredicted operating state for the one or more assets based on the one ormore insights; and configure the dashboard visualization based on thepredicted operating state for the one or more assets.
 3. The system ofclaim 2, the one or more programs further comprising instructionsconfigured to: determine the predicted operating state for the one ormore assets based on one or more relationships between the attributes ofthe aggregated operational technology data.
 4. The system of claim 1,the one or more programs further comprising instructions configured to:determine, based on the one or more insights, solution data for apredicted event associated with the one or more assets, wherein thesolution data includes one or more recommended changes for the one ormore assets to avoid the predicted event; and configure the dashboardvisualization based on the solution data.
 5. The system of claim 1, therequest further comprising a user identifier describing a user role fora user associated with the request, and the one or more programs furthercomprising instructions configured to: filter the visualization data forthe dashboard visualization based on the user identifier.
 6. The systemof claim 1, the request further comprising a metrics context identifierdescribing context for the one or more insights, and method the one ormore programs further comprising instructions configured to: configurethe dashboard visualization based on the metrics context identifier. 7.The system of claim 1, the one or more programs further comprisinginstructions configured to: generate one or more operational limitrecommendations based on the one or more insights; and display one ormore graphical elements associated with the one or more operationallimit recommendations via the dashboard visualization.
 8. The system ofclaim 1, the one or more programs further comprising instructionsconfigured to: generate one or more integrity operating windowrecommendations for the one or more assets based on the one or moreinsights; and display one or more graphical elements associated with theone or more integrity operating window recommendations via the dashboardvisualization.
 9. The system of claim 1, the one or more programsfurther comprising instructions configured to: correlate the attributesof the aggregated operational technology data based on connectionsbetween nodes of the knowledge graph data structure.
 10. A method,comprising: at a device with one or more processors and a memory:receiving a request to generate a dashboard visualization related to oneor more assets, the request comprising: an asset descriptor describingthe one or more assets; and in response to the request: correlating,based on the asset descriptor, features of aggregated operationaltechnology data within a knowledge graph data structure to provide oneor more insights associated with the one or more assets; providing thedashboard visualization to an electronic interface of a computingdevice, the dashboard visualization comprising visualization data forthe one or more insights associated with the knowledge graph datastructure; and adjusting one or more operational settings for the one ormore assets based on the dashboard visualization.
 11. The method ofclaim 10, further comprising: determining a predicted operating statefor the one or more assets based on the one or more insights; andconfiguring the dashboard visualization based on the predicted operatingstate for the one or more assets.
 12. The method of claim 11, furthercomprising: determining the predicted operating state for the one ormore assets based on one or more relationships between the attributes ofthe aggregated operational technology data.
 13. The method of claim 10,further comprising: determining, based on the one or more insights,solution data for a predicted event associated with the one or moreassets, wherein the solution data includes one or more recommendedchanges for the one or more assets to avoid the predicted event; andconfiguring the dashboard visualization based on the solution data. 14.The method of claim 10, the request further comprising a user identifierdescribing a user role for a user associated with the request, and themethod further comprising: filtering the visualization data for thedashboard visualization based on the user identifier.
 15. The method ofclaim 10, the request further comprising a metrics context identifierdescribing context for the one or more insights, and the method furthercomprising: configuring the dashboard visualization based on the metricscontext identifier.
 16. The method of claim 10, further comprising:generating one or more operational limit recommendations based on theone or more insights; and displaying one or more graphical elementsassociated with the one or more operational limit recommendations viathe dashboard visualization.
 17. The method of claim 10, furthercomprising: generating one or more integrity operating windowrecommendations for the one or more assets based on the one or moreinsights; and displaying one or more graphical elements associated withthe one or more integrity operating window recommendations via thedashboard visualization.
 18. A non-transitory computer-readable storagemedium comprising one or more programs for execution by one or moreprocessors of a device, the one or more programs including instructionswhich, when executed by the one or more processors, cause the device to:receive a request to generate a dashboard visualization related to oneor more assets, the request comprising: an asset descriptor describingthe one or more assets; in response to the request: correlate, based onthe asset descriptor, features of aggregated operational technology datawithin a knowledge graph data structure to provide one or more insightsassociated with the one or more assets; and provide the dashboardvisualization to an electronic interface of a computing device, thedashboard visualization comprising visualization data for the one ormore insights associated with the knowledge graph data structure; andadjust one or more operational settings for the one or more assets basedon the dashboard visualization.
 19. The non-transitory computer-readablestorage medium of claim 18, the one or more programs further includinginstructions which, when executed by the one or more processors, causethe device to: determine a predicted operating state for the one or moreassets based on the one or more insights; and configure the dashboardvisualization based on the predicted operating state for the one or moreassets.
 20. The non-transitory computer-readable storage medium of claim18, the one or more programs further including instructions which, whenexecuted by the one or more processors, cause the device to: determine,based on the one or more insights, solution data for a predicted eventassociated with the one or more assets, wherein the solution dataincludes one or more recommended changes for the one or more assets toavoid the predicted event; and configure the dashboard visualizationbased on the solution data.